auto_circuits_humongous.ipynb

{
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  "metadata": {
    "colab": {
      "name": "auto_circuits_humongous.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "nqPSJ6Izm5GZ",
        "EgOLMHAQwd-S",
        "TJlbByAk3buT"
      ],
      "authorship_tag": "ABX9TyPrHB/GxioMIzCfiwej3AbH",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pinballsurgeon/deluxo_adjacency/blob/main/auto_circuits_humongous.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Auto Quantum Circuits\n",
        "#### dehls - pinballsurgeon@gmail.com\n",
        "\n",
        "## Process -\n",
        "  1. Choose vision dataset (James Webb, Bacteria Gram Stains, Wild Fires, or MNIST)\n",
        "  2. Define qubit grid range (ig. 1-5 for free tier colab, 36 total qubits)\n",
        "  3. Define number of experiements you want auto designed and ran\n",
        "  4. Define range of gate combinations (ig. a range of [3-5] would produce random combination of 3, 4 or 5 gates defined in the next step )\n",
        "  5. Define types of possible gate (ig. XX, YY, CNOT, ISWAP ect.)\n",
        "  6. Define Tensorflow epoch, batch size, learnign rate, optimzier. loss and metrics ect.\n",
        "  7. Enjoy and test you quantum ciruit, one which may yet to have ever existed\n",
        "\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "dhwB9OhHwgvh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Installs"
      ],
      "metadata": {
        "id": "nqPSJ6Izm5GZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kL3NaosJFyiz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5cee5d3b-33f0-4e90-89d7-2fae2d93d2d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 10.5 MB 3.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 90 kB 7.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 90 kB 6.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 100 kB 7.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 6.1 MB 12.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.6 MB 31.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 576 kB 41.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 46 kB 1.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 641 kB 16.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 541 kB 44.5 MB/s \n",
            "\u001b[K     |████████████████████████████████| 541 kB 36.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 437 kB 46.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 93 kB 1.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 93 kB 593 kB/s \n",
            "\u001b[K     |████████████████████████████████| 93 kB 920 kB/s \n",
            "\u001b[K     |████████████████████████████████| 93 kB 825 kB/s \n",
            "\u001b[K     |████████████████████████████████| 93 kB 836 kB/s \n",
            "\u001b[K     |████████████████████████████████| 93 kB 1.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 93 kB 979 kB/s \n",
            "\u001b[K     |████████████████████████████████| 93 kB 1.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 93 kB 928 kB/s \n",
            "\u001b[K     |████████████████████████████████| 92 kB 931 kB/s \n",
            "\u001b[K     |████████████████████████████████| 93 kB 701 kB/s \n",
            "\u001b[K     |████████████████████████████████| 93 kB 1.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 93 kB 642 kB/s \n",
            "\u001b[K     |████████████████████████████████| 92 kB 762 kB/s \n",
            "\u001b[K     |████████████████████████████████| 92 kB 482 kB/s \n",
            "\u001b[K     |████████████████████████████████| 92 kB 137 kB/s \n",
            "\u001b[K     |████████████████████████████████| 92 kB 143 kB/s \n",
            "\u001b[K     |████████████████████████████████| 92 kB 9.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 91 kB 7.7 MB/s \n",
            "\u001b[K     |████████████████████████████████| 91 kB 8.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 91 kB 6.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 91 kB 4.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 91 kB 7.9 MB/s \n",
            "\u001b[K     |████████████████████████████████| 91 kB 8.2 MB/s \n",
            "\u001b[K     |████████████████████████████████| 91 kB 6.0 MB/s \n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "pydata-google-auth 1.4.0 requires google-auth<3.0dev,>=1.25.0; python_version >= \"3.6\", but you have google-auth 1.18.0 which is incompatible.\n",
            "google-cloud-bigquery-storage 1.1.2 requires google-api-core[grpc]!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5, but you have google-api-core 1.21.0 which is incompatible.\u001b[0m\n",
            "\u001b[K     |████████████████████████████████| 14.7 MB 5.2 MB/s \n",
            "\u001b[?25hLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gdown in /usr/local/lib/python3.7/dist-packages (4.4.0)\n",
            "Collecting gdown\n",
            "  Downloading gdown-4.5.1.tar.gz (14 kB)\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from gdown) (4.64.0)\n",
            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.7/dist-packages (from gdown) (2.23.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from gdown) (1.15.0)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.7/dist-packages (from gdown) (4.6.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from gdown) (3.7.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown) (2022.6.15)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown) (1.24.3)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
            "Building wheels for collected packages: gdown\n",
            "  Building wheel for gdown (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gdown: filename=gdown-4.5.1-py3-none-any.whl size=14951 sha256=9efb9ae9c045a33a02b2cd4b06d04e82b086ecfff82c952f90618821ef65ad8e\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-i18rnc5e/wheels/3d/ec/b0/a96d1d126183f98570a785e6bf8789fca559853a9260e928e1\n",
            "Successfully built gdown\n",
            "Installing collected packages: gdown\n",
            "  Attempting uninstall: gdown\n",
            "    Found existing installation: gdown 4.4.0\n",
            "    Uninstalling gdown-4.4.0:\n",
            "      Successfully uninstalled gdown-4.4.0\n",
            "Successfully installed gdown-4.5.1\n"
          ]
        }
      ],
      "source": [
        "# tensorflow quantum\n",
        "!pip install --quiet tensorflow_quantum\n",
        "\n",
        "# bloch sphere\n",
        "!pip install --quiet qutip\n",
        "\n",
        "# gdown drive for dans datasets\n",
        "!pip install --upgrade --no-cache-dir gdown"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Imports"
      ],
      "metadata": {
        "id": "EgOLMHAQwd-S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### standard dan-dards\n",
        "import cirq\n",
        "import sympy\n",
        "import tensorflow\n",
        "import numpy\n",
        "import gc\n",
        "import random\n",
        "import gdown\n",
        "import shutil\n",
        "import os\n",
        "from os import listdir\n",
        "import cv2\n",
        "from tensorflow.keras.utils import img_to_array\n",
        "import pandas\n",
        "\n",
        "\n",
        "# metric plotting\n",
        "%matplotlib inline\n",
        "from matplotlib import pyplot\n",
        "from matplotlib.pyplot import figure\n",
        "\n",
        "# tf quantum\n",
        "import tensorflow_quantum\n",
        "\n",
        "# circuit/operations visualizers\n",
        "import qutip\n",
        "from cirq.contrib.svg import SVGCircuit"
      ],
      "metadata": {
        "id": "DUlhaeoeGPop"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Experiment configuration and hyper-parameters"
      ],
      "metadata": {
        "id": "T3MEaAhvLPiZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### Define your quantum machine learning experiment, the values here are optimized for best 'Colab free-tier' experience\n",
        "\n",
        "# which dataset do you want to predict against?\n",
        "# 1 = James Webb vs. Hubble sample classification\n",
        "# 2 = Gram positive vs negative bacteria classification\n",
        "# 3 = Wild fire from space classification\n",
        "# 10 = MNIST written numerical classification\n",
        "dataset_select = 3\n",
        "\n",
        "\n",
        "# qubit grid sizes - how big do you want your circuit's grid size range to be\n",
        "experiment_grid_sizes = [2, 4]\n",
        "\n",
        "# experiment count - how many arrangements of gates, per qubit grid, to run\n",
        "experiment_runs = 5\n",
        "\n",
        "# gate combination range - how many, or few, combination of gates per experiemental run\n",
        "min_gate_cobination = 1\n",
        "max_gate_cobination = 4\n",
        "\n",
        "\n",
        "# cirq gates to hyperparameterize\n",
        "gate_mix = [cirq.CZ\n",
        "           ,cirq.CNOT\n",
        "           ,cirq.XX\n",
        "           ,cirq.ISWAP\n",
        "           ,cirq.ZZ\n",
        "           ,cirq.SWAP\n",
        "           ,cirq.YY\n",
        "           ,cirq.CX]\n",
        "\n",
        "\n",
        "# learning parameters\n",
        "EPOCHS = 50\n",
        "BATCH_SIZE = 5\n",
        "ADAM_OPTIMIZER_LEARNING_RATE = 0.00011\n",
        "\n",
        "# for MINST binary classifier, pick a single digit for TARGET and a single digit for JUNK\n",
        "target_mnist_digit = 3\n",
        "junk_mnist_digit = 4\n",
        "\n"
      ],
      "metadata": {
        "id": "3kA2XNoJLOki"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Helper Functions"
      ],
      "metadata": {
        "id": "TJlbByAk3buT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### Initialize circuit - given a data and out qubit structure, build dynamic circuit\n",
        "class circuit_struct():\n",
        "    def __init__(self, data_qubits, readout):\n",
        "        self.data_qubits = data_qubits\n",
        "        self.readout = readout\n",
        "    \n",
        "    def add_layer(self, circuit, gate, prefix):\n",
        "        for i, qubit in enumerate(self.data_qubits):\n",
        "            symbol = sympy.Symbol(prefix + '-' + str(i))\n",
        "            circuit.append(gate(qubit, self.readout)**symbol)"
      ],
      "metadata": {
        "id": "nvcBT4RIGP3Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### Populate circuit with gates - given a combination of qubit grid size and gate population, build \n",
        "def create_quantum_model(gridSize, hyperGates):\n",
        "\n",
        "    # cirq\n",
        "    data_qubits = cirq.GridQubit.rect(gridSize, gridSize)\n",
        "    readout = cirq.GridQubit(-1, -1)         \n",
        "    circuit = cirq.Circuit()\n",
        "    \n",
        "    # prepare the readout qubit\n",
        "    circuit.append(cirq.X(readout))\n",
        "    circuit.append(cirq.H(readout))\n",
        "    \n",
        "    # create qubit\n",
        "    builder = circuit_struct(\n",
        "        data_qubits = data_qubits,\n",
        "        readout=readout)\n",
        "\n",
        "    # buid layers\n",
        "    for gate in hyperGates:\n",
        "\n",
        "      # add specified gate and name\n",
        "      builder.add_layer(circuit, gate, str(gate))\n",
        "   \n",
        "    # prepare the readout qubit\n",
        "    circuit.append(cirq.H(readout))\n",
        "\n",
        "    return circuit, cirq.Z(readout)"
      ],
      "metadata": {
        "id": "c_eitDLdGQE5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### image pixels to qubit\n",
        "def convert_to_circuit(image):\n",
        "\n",
        "    # flatten array\n",
        "    values = numpy.ndarray.flatten(image)\n",
        "    \n",
        "    # establish 4x4 circuit\n",
        "    qubits = cirq.GridQubit.rect(4, 4)\n",
        "    circuit = cirq.Circuit()\n",
        "\n",
        "    # \n",
        "    for i, value in enumerate(values):\n",
        "        if value:\n",
        "            circuit.append(cirq.X(qubits[i]))\n",
        "    return circuit"
      ],
      "metadata": {
        "id": "ISAOn5Aa-CXb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### this example is for binary classification, so only keep one MNIST class/digit as target, and then one other as NON-TARGET/JUNK\n",
        "def binary_filter(x, y):\n",
        "\n",
        "    # identify rows that will be kept\n",
        "    keep = (y == target_mnist_digit) | (y == junk_mnist_digit)\n",
        "    \n",
        "    # remove excess\n",
        "    x, y = x[keep], y[keep]\n",
        "\n",
        "    # establish target 1/0\n",
        "    y = y == target_mnist_digit\n",
        " \n",
        "    return x,y"
      ],
      "metadata": {
        "id": "A68jGN3KPo5F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### reveal experiment results per run\n",
        "def experiment_reveal(log, run):\n",
        "\n",
        "  fig, (ax1, ax2) = pyplot.subplots(2)\n",
        "  pyplot.title('MSE Loss and Accuracy of %s gated Parametrized Quantum Circuit' % (log[run]['gates']), fontdict = {'fontsize' :16})\n",
        "\n",
        "  ax1.plot(log[run]['history'].history['loss'])\n",
        "  ax1.plot(log[run]['history'].history['val_loss'])\n",
        "  ax1.set_ylabel('MAPL')\n",
        "\n",
        "  ax2.plot(log[run]['history'].history['mean_squared_error'])\n",
        "  ax2.plot(log[run]['history'].history['val_mean_squared_error'] )\n",
        "  ax2.set_ylabel('MSE')\n",
        "  ax2.set_xlabel('Epochs')\n",
        "  \n",
        "  fig = pyplot.gcf()\n",
        "  fig.set_size_inches(12.5, 7.5)\n",
        "\n",
        "  return log[run]['circuit']\n"
      ],
      "metadata": {
        "id": "oukNDFyXTTec"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### import dan ehlers' novel supplied datasource\n",
        "def dans_data(url, path):\n",
        "\n",
        "  # if directory exists, delete as to make room\n",
        "  if os.path.isdir(path):\n",
        "    shutil.rmtree(path, ignore_errors=True)\n",
        "\n",
        "  # download from dan's drive, yay security exposure\n",
        "  gdown.download_folder(url, quiet=True)\n",
        "\n",
        "  return path"
      ],
      "metadata": {
        "id": "fmrKdbyeDfiD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Build experiment circuits and gates"
      ],
      "metadata": {
        "id": "Yhv6ZWH4mcpf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### build experiment\n",
        "\n",
        "experiment_design = []\n",
        "\n",
        "# given experiement design parameters, build experiment(s)\n",
        "for run in range(1, experiment_runs+1):\n",
        "\n",
        "  # gate combination\n",
        "  gate_combination = random.choices(gate_mix, k=random.randrange(min_gate_cobination, max_gate_cobination+1))\n",
        "\n",
        "  # print random experiment gate participants\n",
        "  print('Experiment %d Gates:' % run); print(gate_combination); print('')\n",
        "\n",
        "  # build experiment\n",
        "  experiment_design.append(gate_combination)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BVngC51whQjI",
        "outputId": "d573ed6f-e1d7-4caf-bf0a-ad7c92ac017a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Experiment 1 Gates:\n",
            "[cirq.XX]\n",
            "\n",
            "Experiment 2 Gates:\n",
            "[cirq.ISWAP]\n",
            "\n",
            "Experiment 3 Gates:\n",
            "[cirq.SWAP, cirq.CNOT, cirq.CNOT]\n",
            "\n",
            "Experiment 4 Gates:\n",
            "[cirq.SWAP, cirq.ZZ, cirq.CZ, cirq.SWAP]\n",
            "\n",
            "Experiment 5 Gates:\n",
            "[cirq.YY, cirq.XX, cirq.CNOT]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Load Dataset(s)"
      ],
      "metadata": {
        "id": "muRPuaO3S4EZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### download data source as specified, manage datasource specific handlers here\n",
        "\n",
        "# download drive share, per datasource selection\n",
        "if dataset_select == 1:              # james webb vs. hubble telescope classification\n",
        "  paths = dans_data(\"https://drive.google.com/drive/folders/1Ae4sZyMvJ02ht5O6UEBCpWg2R1e_Ihr3?usp=sharing\",'/content/tele_source')\n",
        "  trg_label_prefix = 'jw_'\n",
        "\n",
        "elif dataset_select == 2:            # bacteria gram positive vs negative classification\n",
        "  paths = dans_data(\"https://drive.google.com/drive/folders/1hhx1mEfWz-ThviwOqXK63v3wy2J7xV2c?usp=sharing\",'/content/bact_source')\n",
        "  trg_label_prefix = 'pos_'\n",
        "\n",
        "elif dataset_select == 3:            # wild fire classification\n",
        "  paths = dans_data(\"https://drive.google.com/drive/folders/1KRKEq8ZH7aKIVDRZQjknpEza2TnhiRxw?usp=sharing\",'/content/fire_source')\n",
        "  trg_label_prefix = 'pos_'\n"
      ],
      "metadata": {
        "id": "qZbwqvM-Y2NK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### ingest and process dehls created datasets\n",
        "imagePaths = [paths +'/' + f for f in listdir(paths)]\n",
        "data = []\n",
        "labels = []\n",
        "\n",
        "imagePaths = sorted(list(imagePaths))\n",
        "print(imagePaths)\n",
        "\n",
        "# loop over the input images\n",
        "for imagePath in imagePaths:\n",
        "    # load the image, pre-process it, and store it in the data list\n",
        "    image = cv2.imread(imagePath)\n",
        "    image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
        "    image = cv2.resize(image, (28, 28))\n",
        "    image = img_to_array(image)\n",
        "    data.append(image)\n",
        "\n",
        "    if trg_label_prefix in imagePath:\n",
        "      labels.append(1)\n",
        "    else:\n",
        "      labels.append(0)\n",
        "\n",
        "data = numpy.array(data, dtype=\"float\") / 255.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ce7ufFJ6IaQ6",
        "outputId": "606ab6c5-8b25-4967-ba2d-1dd5cd8ca35d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['/content/fire_source/neg_1.png', '/content/fire_source/neg_10.png', '/content/fire_source/neg_11.png', '/content/fire_source/neg_12.png', '/content/fire_source/neg_13.png', '/content/fire_source/neg_14.png', '/content/fire_source/neg_15.png', '/content/fire_source/neg_16.png', '/content/fire_source/neg_17.png', '/content/fire_source/neg_18.png', '/content/fire_source/neg_19.png', '/content/fire_source/neg_2.png', '/content/fire_source/neg_20.png', '/content/fire_source/neg_3.png', '/content/fire_source/neg_4.png', '/content/fire_source/neg_5.png', '/content/fire_source/neg_6.png', '/content/fire_source/neg_7.png', '/content/fire_source/neg_8.png', '/content/fire_source/neg_9.png', '/content/fire_source/pos_01.png', '/content/fire_source/pos_02.png', '/content/fire_source/pos_03.png', '/content/fire_source/pos_04.png', '/content/fire_source/pos_05.png', '/content/fire_source/pos_06.png', '/content/fire_source/pos_07.png', '/content/fire_source/pos_08.png', '/content/fire_source/pos_09.png', '/content/fire_source/pos_10.png', '/content/fire_source/pos_11.png', '/content/fire_source/pos_12.png', '/content/fire_source/pos_13.png', '/content/fire_source/pos_14.png', '/content/fire_source/pos_15.png', '/content/fire_source/pos_16.png', '/content/fire_source/pos_17.png', '/content/fire_source/pos_18.png', '/content/fire_source/pos_19.png', '/content/fire_source/pos_20.png']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### establish dataset, here the MNIST numerical yada-yada\n",
        "\n",
        "# 1. import mnist digits\n",
        "(x_train, y_train), (x_test, y_test) = tensorflow.keras.datasets.mnist.load_data()\n",
        "\n",
        "# 2. rescale images to [0 - 1]\n",
        "x_train, x_test = x_train[..., numpy.newaxis]/255.0, x_test[..., numpy.newaxis]/255.0\n",
        "\n",
        "# 3. quantum experiment is binary classification, so only keep two classes/digit examples\n",
        "x_train, y_train = binary_filter(x_train, y_train)\n",
        "x_test, y_test = binary_filter(x_test, y_test)\n",
        "\n",
        "# 4. change dataset from which to predict against, with MANUAL CONVOLUTIONS\n",
        "if (dataset_select < 10):       # non MNIST sources\n",
        "  x_train = data + [numpy.flip(x) for x in data] + [numpy.rot90(x) for x in data] + [numpy.fliplr(x) for x in data] + [numpy.flipud(x) for x in data]\n",
        "  x_test = [numpy.rot90(x) for x in data]\n",
        "  y_train = numpy.array(labels) + numpy.array(labels) + numpy.array(labels) + numpy.array(labels) + numpy.array(labels)\n",
        "\n",
        "  y_test = numpy.array(labels)\n",
        "\n",
        "# 5. resize image\n",
        "x_train_small = tensorflow.image.resize(x_train, (4,4)).numpy()\n",
        "x_test_small = tensorflow.image.resize(x_test, (4,4)).numpy()\n",
        "\n",
        "# 6. convert circuit pixels/cells into individual qubits\n",
        "x_train_circ = [convert_to_circuit(x) for x in x_train_small]\n",
        "x_test_circ = [convert_to_circuit(x) for x in x_test_small]\n",
        "\n",
        "# 7. convert to qubit imagery into tensors\n",
        "x_train_tfcirc = tensorflow_quantum.convert_to_tensor(x_train_circ)\n",
        "x_test_tfcirc = tensorflow_quantum.convert_to_tensor(x_test_circ)"
      ],
      "metadata": {
        "id": "vHHqySURQyHH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Tensorflow quantum network"
      ],
      "metadata": {
        "id": "e672kQ6BTFgn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "### tensorflow callbacks, here specifically garbage collection as to reduce memory load below colab free tier\n",
        "class mem_release(tensorflow.keras.callbacks.Callback):\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        gc.collect()\n"
      ],
      "metadata": {
        "id": "UsvRncZpRsSN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### tensorflow quantum experimental gate combinatorial iterator \n",
        "\n",
        "# keep track of experiment conditions and results\n",
        "experiment_count = 0\n",
        "experiment_log = {}\n",
        "\n",
        "\n",
        "for gates in experiment_design:\n",
        "  for gridSize_int in experiment_grid_sizes:\n",
        "\n",
        "\n",
        "    # initialize quantum circuit\n",
        "    model_circuit, model_readout = create_quantum_model(gridSize_int, gates)\n",
        "\n",
        "\n",
        "    # build tf model\n",
        "    model = tensorflow.keras.Sequential([\n",
        "        \n",
        "        # input data circuit as tf.string\n",
        "        tensorflow.keras.layers.Input(shape=(), dtype=tensorflow.string),\n",
        "\n",
        "        # parameterized quantum circuit\n",
        "        tensorflow_quantum.layers.PQC(model_circuit, model_readout),])\n",
        "\n",
        "\n",
        "    # compile model and determine loss / optimizer approach, for this introductory we'll focus on memory\n",
        "    model.compile(\n",
        "\n",
        "        # loss, mean square error\n",
        "        # loss=tensorflow.keras.losses.binary_crossentropy,\n",
        "        loss=tensorflow.keras.losses.mean_absolute_percentage_error,\n",
        "\n",
        "        # optimizer, Adam with specifiable learning rate\n",
        "        optimizer=tensorflow.keras.optimizers.Adam(learning_rate=ADAM_OPTIMIZER_LEARNING_RATE),\n",
        "      \n",
        "        # metrics, accuracy\n",
        "        metrics=[tensorflow.keras.metrics.mean_absolute_percentage_error\n",
        "               , tensorflow.keras.metrics.mean_absolute_error\n",
        "               , tensorflow.keras.metrics.mean_squared_error\n",
        "               ]\n",
        "      )\n",
        "    \n",
        "    # display model parameters\n",
        "    print(''); print(''.join(str(gates))); print(model.summary())\n",
        "\n",
        "    # train model for experiment, define training and validation populations, callbacks ect.\n",
        "    experiment_history = model.fit(\n",
        "        x_train_tfcirc,\n",
        "        y_train,\n",
        "        batch_size=BATCH_SIZE,\n",
        "        epochs=EPOCHS,\n",
        "        verbose=1,\n",
        "        callbacks=[mem_release()\n",
        "                  ,tensorflow.keras.callbacks.EarlyStopping(monitor='loss', patience=3)],\n",
        "        validation_data=(x_test_tfcirc, y_test)\n",
        "        )\n",
        "\n",
        "    # after training, evaluate final model representation against test set\n",
        "    experiment_results = model.evaluate(x_test_tfcirc, y_test)\n",
        "\n",
        "    # record experiment\n",
        "    experiment_count += 1\n",
        "    experiment_log[experiment_count] = {'circuit' : model_circuit       \n",
        "                                       ,'gates'   : gates\n",
        "                                       ,'history' : experiment_history\n",
        "                                       ,'results' : experiment_results }\n",
        "    "
      ],
      "metadata": {
        "id": "dPcJoEuGGgoN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d48a1558-7002-467b-8ebb-fdd802732a72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "[cirq.XX]\n",
            "Model: \"sequential_56\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " pqc_56 (PQC)                (None, 1)                 4         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4\n",
            "Trainable params: 4\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/50\n",
            "8/8 [==============================] - 18s 3s/step - loss: 499999936.0000 - mean_absolute_percentage_error: 499999936.0000 - mean_absolute_error: 3.5000 - mean_squared_error: 18.5000 - val_loss: 499999840.0000 - val_mean_absolute_percentage_error: 499999840.0000 - val_mean_absolute_error: 1.5000 - val_mean_squared_error: 2.5000\n",
            "Epoch 2/50\n",
            "8/8 [==============================] - 2s 236ms/step - loss: 499999904.0000 - mean_absolute_percentage_error: 499999904.0000 - mean_absolute_error: 3.5000 - mean_squared_error: 18.5000 - val_loss: 500000064.0000 - val_mean_absolute_percentage_error: 500000064.0000 - val_mean_absolute_error: 1.5000 - val_mean_squared_error: 2.5000\n",
            "Epoch 3/50\n",
            "8/8 [==============================] - 2s 236ms/step - loss: 500000064.0000 - mean_absolute_percentage_error: 500000064.0000 - mean_absolute_error: 3.5000 - mean_squared_error: 18.5000 - val_loss: 499999808.0000 - val_mean_absolute_percentage_error: 499999808.0000 - val_mean_absolute_error: 1.5000 - val_mean_squared_error: 2.5000\n",
            "Epoch 4/50\n",
            "8/8 [==============================] - 2s 232ms/step - loss: 499999936.0000 - mean_absolute_percentage_error: 499999936.0000 - mean_absolute_error: 3.5000 - mean_squared_error: 18.5000 - val_loss: 499999904.0000 - val_mean_absolute_percentage_error: 499999904.0000 - val_mean_absolute_error: 1.5000 - val_mean_squared_error: 2.5000\n",
            "Epoch 5/50\n",
            "8/8 [==============================] - 2s 233ms/step - loss: 499999936.0000 - mean_absolute_percentage_error: 499999936.0000 - mean_absolute_error: 3.5000 - mean_squared_error: 18.5000 - val_loss: 499999904.0000 - val_mean_absolute_percentage_error: 499999904.0000 - val_mean_absolute_error: 1.5000 - val_mean_squared_error: 2.5000\n",
            "2/2 [==============================] - 0s 58ms/step - loss: 500000064.0000 - mean_absolute_percentage_error: 500000064.0000 - mean_absolute_error: 1.5000 - mean_squared_error: 2.5000\n",
            "\n",
            "[cirq.XX]\n",
            "Model: \"sequential_57\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " pqc_57 (PQC)                (None, 1)                 16        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 16\n",
            "Trainable params: 16\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/50\n",
            "8/8 [==============================] - 3s 377ms/step - loss: 500000160.0000 - mean_absolute_percentage_error: 500000160.0000 - mean_absolute_error: 3.5000 - mean_squared_error: 18.5000 - val_loss: 499999936.0000 - val_mean_absolute_percentage_error: 499999936.0000 - val_mean_absolute_error: 1.5000 - val_mean_squared_error: 2.5000\n",
            "Epoch 2/50\n",
            "8/8 [==============================] - 3s 393ms/step - loss: 500000000.0000 - mean_absolute_percentage_error: 500000000.0000 - mean_absolute_error: 3.5000 - mean_squared_error: 18.5000 - val_loss: 500000064.0000 - val_mean_absolute_percentage_error: 500000064.0000 - val_mean_absolute_error: 1.5000 - val_mean_squared_error: 2.5000\n",
            "Epoch 3/50\n",
            "8/8 [==============================] - 3s 424ms/step - loss: 500000096.0000 - mean_absolute_percentage_error: 500000096.0000 - mean_absolute_error: 3.5000 - mean_squared_error: 18.5000 - val_loss: 499999840.0000 - val_mean_absolute_percentage_error: 499999840.0000 - val_mean_absolute_error: 1.5000 - val_mean_squared_error: 2.5000\n",
            "Epoch 4/50\n",
            "8/8 [==============================] - 3s 346ms/step - loss: 500000064.0000 - mean_absolute_percentage_error: 500000064.0000 - mean_absolute_error: 3.5000 - mean_squared_error: 18.5000 - val_loss: 500000064.0000 - val_mean_absolute_percentage_error: 500000064.0000 - val_mean_absolute_error: 1.5000 - val_mean_squared_error: 2.5000\n",
            "Epoch 5/50\n",
            "8/8 [==============================] - 3s 345ms/step - loss: 500000096.0000 - mean_absolute_percentage_error: 500000096.0000 - mean_absolute_error: 3.5000 - mean_squared_error: 18.5000 - val_loss: 500000064.0000 - val_mean_absolute_percentage_error: 500000064.0000 - val_mean_absolute_error: 1.5000 - val_mean_squared_error: 2.5000\n",
            "2/2 [==============================] - 1s 73ms/step - loss: 500000192.0000 - mean_absolute_percentage_error: 500000192.0000 - mean_absolute_error: 1.5000 - mean_squared_error: 2.5000\n",
            "\n",
            "[cirq.ISWAP]\n",
            "Model: \"sequential_58\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " pqc_58 (PQC)                (None, 1)                 4         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4\n",
            "Trainable params: 4\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/50\n",
            "8/8 [==============================] - 3s 265ms/step - loss: 162759504.0000 - mean_absolute_percentage_error: 162759504.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 10.9783 - val_loss: 162364448.0000 - val_mean_absolute_percentage_error: 162364448.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2807\n",
            "Epoch 2/50\n",
            "8/8 [==============================] - 2s 234ms/step - loss: 162066896.0000 - mean_absolute_percentage_error: 162066896.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 10.9847 - val_loss: 161652000.0000 - val_mean_absolute_percentage_error: 161652000.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2812\n",
            "Epoch 3/50\n",
            "8/8 [==============================] - 2s 236ms/step - loss: 161318240.0000 - mean_absolute_percentage_error: 161318240.0000 - mean_absolute_error: 2.4999 - mean_squared_error: 10.9901 - val_loss: 160977904.0000 - val_mean_absolute_percentage_error: 160977904.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2817\n",
            "Epoch 4/50\n",
            "8/8 [==============================] - 2s 236ms/step - loss: 160661072.0000 - mean_absolute_percentage_error: 160661072.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 10.9964 - val_loss: 160275056.0000 - val_mean_absolute_percentage_error: 160275056.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2822\n",
            "Epoch 5/50\n",
            "8/8 [==============================] - 2s 237ms/step - loss: 159973984.0000 - mean_absolute_percentage_error: 159973984.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.0027 - val_loss: 159575920.0000 - val_mean_absolute_percentage_error: 159575920.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2827\n",
            "Epoch 6/50\n",
            "8/8 [==============================] - 2s 233ms/step - loss: 159268064.0000 - mean_absolute_percentage_error: 159268064.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.0086 - val_loss: 158885504.0000 - val_mean_absolute_percentage_error: 158885504.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2832\n",
            "Epoch 7/50\n",
            "8/8 [==============================] - 2s 237ms/step - loss: 158602192.0000 - mean_absolute_percentage_error: 158602192.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 11.0151 - val_loss: 158182640.0000 - val_mean_absolute_percentage_error: 158182640.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2837\n",
            "Epoch 8/50\n",
            "8/8 [==============================] - 2s 236ms/step - loss: 157853200.0000 - mean_absolute_percentage_error: 157853200.0000 - mean_absolute_error: 2.4999 - mean_squared_error: 11.0204 - val_loss: 157516512.0000 - val_mean_absolute_percentage_error: 157516512.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2842\n",
            "Epoch 9/50\n",
            "8/8 [==============================] - 2s 234ms/step - loss: 157197520.0000 - mean_absolute_percentage_error: 157197520.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.0266 - val_loss: 156822400.0000 - val_mean_absolute_percentage_error: 156822400.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2847\n",
            "Epoch 10/50\n",
            "8/8 [==============================] - 2s 233ms/step - loss: 156498448.0000 - mean_absolute_percentage_error: 156498448.0000 - mean_absolute_error: 2.4999 - mean_squared_error: 11.0325 - val_loss: 156133856.0000 - val_mean_absolute_percentage_error: 156133856.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2852\n",
            "Epoch 11/50\n",
            "8/8 [==============================] - 2s 234ms/step - loss: 155803280.0000 - mean_absolute_percentage_error: 155803280.0000 - mean_absolute_error: 2.4999 - mean_squared_error: 11.0384 - val_loss: 155447584.0000 - val_mean_absolute_percentage_error: 155447584.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2858\n",
            "Epoch 12/50\n",
            "8/8 [==============================] - 2s 238ms/step - loss: 155143536.0000 - mean_absolute_percentage_error: 155143536.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.0449 - val_loss: 154737840.0000 - val_mean_absolute_percentage_error: 154737840.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2863\n",
            "Epoch 13/50\n",
            "8/8 [==============================] - 2s 236ms/step - loss: 154432736.0000 - mean_absolute_percentage_error: 154432736.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.0510 - val_loss: 154044528.0000 - val_mean_absolute_percentage_error: 154044528.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2868\n",
            "Epoch 14/50\n",
            "8/8 [==============================] - 2s 234ms/step - loss: 153717312.0000 - mean_absolute_percentage_error: 153717312.0000 - mean_absolute_error: 2.4999 - mean_squared_error: 11.0567 - val_loss: 153369632.0000 - val_mean_absolute_percentage_error: 153369632.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2873\n",
            "Epoch 15/50\n",
            "8/8 [==============================] - 2s 232ms/step - loss: 153077440.0000 - mean_absolute_percentage_error: 153077440.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.0633 - val_loss: 152657184.0000 - val_mean_absolute_percentage_error: 152657184.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2879\n",
            "Epoch 16/50\n",
            "8/8 [==============================] - 2s 234ms/step - loss: 152373296.0000 - mean_absolute_percentage_error: 152373296.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 11.0697 - val_loss: 151962096.0000 - val_mean_absolute_percentage_error: 151962096.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2884\n",
            "Epoch 17/50\n",
            "8/8 [==============================] - 2s 232ms/step - loss: 151657680.0000 - mean_absolute_percentage_error: 151657680.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.0753 - val_loss: 151286624.0000 - val_mean_absolute_percentage_error: 151286624.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2890\n",
            "Epoch 18/50\n",
            "8/8 [==============================] - 2s 240ms/step - loss: 151017248.0000 - mean_absolute_percentage_error: 151017232.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 11.0819 - val_loss: 150580800.0000 - val_mean_absolute_percentage_error: 150580800.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2895\n",
            "Epoch 19/50\n",
            "8/8 [==============================] - 2s 236ms/step - loss: 150301280.0000 - mean_absolute_percentage_error: 150301280.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.0878 - val_loss: 149904096.0000 - val_mean_absolute_percentage_error: 149904096.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2901\n",
            "Epoch 20/50\n",
            "8/8 [==============================] - 2s 237ms/step - loss: 149574736.0000 - mean_absolute_percentage_error: 149574736.0000 - mean_absolute_error: 2.4999 - mean_squared_error: 11.0927 - val_loss: 149264304.0000 - val_mean_absolute_percentage_error: 149264304.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2906\n",
            "Epoch 21/50\n",
            "8/8 [==============================] - 2s 237ms/step - loss: 148962736.0000 - mean_absolute_percentage_error: 148962736.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.0991 - val_loss: 148571392.0000 - val_mean_absolute_percentage_error: 148571392.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2912\n",
            "Epoch 22/50\n",
            "8/8 [==============================] - 2s 237ms/step - loss: 148295888.0000 - mean_absolute_percentage_error: 148295888.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 11.1056 - val_loss: 147874208.0000 - val_mean_absolute_percentage_error: 147874208.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2917\n",
            "Epoch 23/50\n",
            "8/8 [==============================] - 2s 235ms/step - loss: 147581536.0000 - mean_absolute_percentage_error: 147581536.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.1113 - val_loss: 147205728.0000 - val_mean_absolute_percentage_error: 147205728.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2923\n",
            "Epoch 24/50\n",
            "8/8 [==============================] - 2s 235ms/step - loss: 146918016.0000 - mean_absolute_percentage_error: 146918016.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.1173 - val_loss: 146528080.0000 - val_mean_absolute_percentage_error: 146528080.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2928\n",
            "Epoch 25/50\n",
            "8/8 [==============================] - 2s 234ms/step - loss: 146220224.0000 - mean_absolute_percentage_error: 146220224.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.1229 - val_loss: 145865888.0000 - val_mean_absolute_percentage_error: 145865888.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2934\n",
            "Epoch 26/50\n",
            "8/8 [==============================] - 2s 234ms/step - loss: 145601376.0000 - mean_absolute_percentage_error: 145601376.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 11.1297 - val_loss: 145161408.0000 - val_mean_absolute_percentage_error: 145161408.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2940\n",
            "Epoch 27/50\n",
            "8/8 [==============================] - 2s 236ms/step - loss: 144883536.0000 - mean_absolute_percentage_error: 144883536.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.1355 - val_loss: 144492416.0000 - val_mean_absolute_percentage_error: 144492416.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2945\n",
            "Epoch 28/50\n",
            "8/8 [==============================] - 2s 237ms/step - loss: 144167344.0000 - mean_absolute_percentage_error: 144167344.0000 - mean_absolute_error: 2.4999 - mean_squared_error: 11.1404 - val_loss: 143858624.0000 - val_mean_absolute_percentage_error: 143858624.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2951\n",
            "Epoch 29/50\n",
            "8/8 [==============================] - 2s 240ms/step - loss: 143554080.0000 - mean_absolute_percentage_error: 143554080.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.1466 - val_loss: 143177648.0000 - val_mean_absolute_percentage_error: 143177648.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2956\n",
            "Epoch 30/50\n",
            "8/8 [==============================] - 2s 237ms/step - loss: 142884752.0000 - mean_absolute_percentage_error: 142884752.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.1529 - val_loss: 142495648.0000 - val_mean_absolute_percentage_error: 142495648.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2962\n",
            "Epoch 31/50\n",
            "8/8 [==============================] - 2s 235ms/step - loss: 142245792.0000 - mean_absolute_percentage_error: 142245792.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 11.1596 - val_loss: 141796928.0000 - val_mean_absolute_percentage_error: 141796928.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2968\n",
            "Epoch 32/50\n",
            "8/8 [==============================] - 2s 238ms/step - loss: 141523168.0000 - mean_absolute_percentage_error: 141523168.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.1651 - val_loss: 141138384.0000 - val_mean_absolute_percentage_error: 141138384.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2974\n",
            "Epoch 33/50\n",
            "8/8 [==============================] - 2s 235ms/step - loss: 140845568.0000 - mean_absolute_percentage_error: 140845568.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.1707 - val_loss: 140488448.0000 - val_mean_absolute_percentage_error: 140488448.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2980\n",
            "Epoch 34/50\n",
            "8/8 [==============================] - 2s 238ms/step - loss: 140201200.0000 - mean_absolute_percentage_error: 140201200.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.1766 - val_loss: 139820320.0000 - val_mean_absolute_percentage_error: 139820320.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2986\n",
            "Epoch 35/50\n",
            "8/8 [==============================] - 2s 235ms/step - loss: 139518880.0000 - mean_absolute_percentage_error: 139518880.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.1823 - val_loss: 139163952.0000 - val_mean_absolute_percentage_error: 139163952.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2991\n",
            "Epoch 36/50\n",
            "8/8 [==============================] - 2s 235ms/step - loss: 138890656.0000 - mean_absolute_percentage_error: 138890656.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 11.1888 - val_loss: 138478640.0000 - val_mean_absolute_percentage_error: 138478640.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2997\n",
            "Epoch 37/50\n",
            "8/8 [==============================] - 2s 240ms/step - loss: 138225584.0000 - mean_absolute_percentage_error: 138225584.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 11.1951 - val_loss: 137794608.0000 - val_mean_absolute_percentage_error: 137794608.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3004\n",
            "Epoch 38/50\n",
            "8/8 [==============================] - 3s 380ms/step - loss: 137520112.0000 - mean_absolute_percentage_error: 137520112.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.2007 - val_loss: 137143328.0000 - val_mean_absolute_percentage_error: 137143328.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3009\n",
            "Epoch 39/50\n",
            "8/8 [==============================] - 2s 239ms/step - loss: 136848608.0000 - mean_absolute_percentage_error: 136848608.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.2062 - val_loss: 136498400.0000 - val_mean_absolute_percentage_error: 136498400.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3015\n",
            "Epoch 40/50\n",
            "8/8 [==============================] - 2s 239ms/step - loss: 136199200.0000 - mean_absolute_percentage_error: 136199200.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.2120 - val_loss: 135842768.0000 - val_mean_absolute_percentage_error: 135842768.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3021\n",
            "Epoch 41/50\n",
            "8/8 [==============================] - 2s 237ms/step - loss: 135534208.0000 - mean_absolute_percentage_error: 135534240.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.2177 - val_loss: 135188160.0000 - val_mean_absolute_percentage_error: 135188160.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3027\n",
            "Epoch 42/50\n",
            "8/8 [==============================] - 2s 235ms/step - loss: 134848352.0000 - mean_absolute_percentage_error: 134848352.0000 - mean_absolute_error: 2.4999 - mean_squared_error: 11.2230 - val_loss: 134554528.0000 - val_mean_absolute_percentage_error: 134554528.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3033\n",
            "Epoch 43/50\n",
            "8/8 [==============================] - 2s 239ms/step - loss: 134264352.0000 - mean_absolute_percentage_error: 134264352.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.2298 - val_loss: 133860248.0000 - val_mean_absolute_percentage_error: 133860248.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3040\n",
            "Epoch 44/50\n",
            "8/8 [==============================] - 2s 236ms/step - loss: 133549616.0000 - mean_absolute_percentage_error: 133549616.0000 - mean_absolute_error: 2.4999 - mean_squared_error: 11.2353 - val_loss: 133208272.0000 - val_mean_absolute_percentage_error: 133208272.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3046\n",
            "Epoch 45/50\n",
            "8/8 [==============================] - 2s 235ms/step - loss: 132878552.0000 - mean_absolute_percentage_error: 132878552.0000 - mean_absolute_error: 2.4999 - mean_squared_error: 11.2408 - val_loss: 132566184.0000 - val_mean_absolute_percentage_error: 132566184.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3052\n",
            "Epoch 46/50\n",
            "8/8 [==============================] - 2s 237ms/step - loss: 132284272.0000 - mean_absolute_percentage_error: 132284272.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.2476 - val_loss: 131872944.0000 - val_mean_absolute_percentage_error: 131872944.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3058\n",
            "Epoch 47/50\n",
            "8/8 [==============================] - 2s 237ms/step - loss: 131601232.0000 - mean_absolute_percentage_error: 131601232.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 11.2538 - val_loss: 131200688.0000 - val_mean_absolute_percentage_error: 131200688.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3065\n",
            "Epoch 48/50\n",
            "8/8 [==============================] - 2s 235ms/step - loss: 130930192.0000 - mean_absolute_percentage_error: 130930192.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.2596 - val_loss: 130539840.0000 - val_mean_absolute_percentage_error: 130539840.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3071\n",
            "Epoch 49/50\n",
            "8/8 [==============================] - 2s 237ms/step - loss: 130266368.0000 - mean_absolute_percentage_error: 130266368.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.2655 - val_loss: 129886120.0000 - val_mean_absolute_percentage_error: 129886120.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3077\n",
            "Epoch 50/50\n",
            "8/8 [==============================] - 2s 235ms/step - loss: 129622320.0000 - mean_absolute_percentage_error: 129622320.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 11.2716 - val_loss: 129223960.0000 - val_mean_absolute_percentage_error: 129223960.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3083\n",
            "2/2 [==============================] - 0s 60ms/step - loss: 129223952.0000 - mean_absolute_percentage_error: 129223952.0000 - mean_absolute_error: 0.5000 - mean_squared_error: 0.3083\n",
            "\n",
            "[cirq.ISWAP]\n",
            "Model: \"sequential_59\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " pqc_59 (PQC)                (None, 1)                 16        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 16\n",
            "Trainable params: 16\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/50\n",
            "8/8 [==============================] - 3s 376ms/step - loss: 13285.9561 - mean_absolute_percentage_error: 13285.9561 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 12969.3066 - val_mean_absolute_percentage_error: 12969.3066 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 2/50\n",
            "8/8 [==============================] - 3s 351ms/step - loss: 12703.3223 - mean_absolute_percentage_error: 12703.3223 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 12403.0625 - val_mean_absolute_percentage_error: 12403.0625 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 3/50\n",
            "8/8 [==============================] - 4s 513ms/step - loss: 12189.9746 - mean_absolute_percentage_error: 12189.9746 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 11836.8184 - val_mean_absolute_percentage_error: 11836.8184 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 4/50\n",
            "8/8 [==============================] - 3s 348ms/step - loss: 11615.5361 - mean_absolute_percentage_error: 11615.5361 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 11345.0811 - val_mean_absolute_percentage_error: 11345.0811 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 5/50\n",
            "8/8 [==============================] - 3s 346ms/step - loss: 11108.8975 - mean_absolute_percentage_error: 11108.8984 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 10853.3428 - val_mean_absolute_percentage_error: 10853.3428 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 6/50\n",
            "8/8 [==============================] - 3s 352ms/step - loss: 10658.1377 - mean_absolute_percentage_error: 10658.1377 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 10376.5049 - val_mean_absolute_percentage_error: 10376.5049 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 7/50\n",
            "8/8 [==============================] - 3s 347ms/step - loss: 10170.1240 - mean_absolute_percentage_error: 10170.1240 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 9914.5684 - val_mean_absolute_percentage_error: 9914.5684 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 8/50\n",
            "8/8 [==============================] - 3s 349ms/step - loss: 9721.5996 - mean_absolute_percentage_error: 9721.5996 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 9497.3359 - val_mean_absolute_percentage_error: 9497.3359 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 9/50\n",
            "8/8 [==============================] - 3s 349ms/step - loss: 9351.3047 - mean_absolute_percentage_error: 9351.3047 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 9080.1035 - val_mean_absolute_percentage_error: 9080.1035 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 10/50\n",
            "8/8 [==============================] - 3s 348ms/step - loss: 8923.1758 - mean_absolute_percentage_error: 8923.1758 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 8709.4375 - val_mean_absolute_percentage_error: 8709.4375 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 11/50\n",
            "8/8 [==============================] - 3s 345ms/step - loss: 8538.7490 - mean_absolute_percentage_error: 8538.7490 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 8366.7109 - val_mean_absolute_percentage_error: 8366.7109 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 12/50\n",
            "8/8 [==============================] - 3s 350ms/step - loss: 8204.2188 - mean_absolute_percentage_error: 8204.2188 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 8009.0830 - val_mean_absolute_percentage_error: 8009.0830 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 13/50\n",
            "8/8 [==============================] - 3s 348ms/step - loss: 7873.4062 - mean_absolute_percentage_error: 7873.4062 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 7696.1875 - val_mean_absolute_percentage_error: 7696.1875 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 14/50\n",
            "8/8 [==============================] - 3s 348ms/step - loss: 7560.5859 - mean_absolute_percentage_error: 7560.5859 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 7368.3618 - val_mean_absolute_percentage_error: 7368.3618 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 15/50\n",
            "8/8 [==============================] - 3s 347ms/step - loss: 7239.4658 - mean_absolute_percentage_error: 7239.4658 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 7100.1416 - val_mean_absolute_percentage_error: 7100.1416 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 16/50\n",
            "8/8 [==============================] - 3s 348ms/step - loss: 6947.2852 - mean_absolute_percentage_error: 6947.2852 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 6816.5537 - val_mean_absolute_percentage_error: 6816.5537 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 17/50\n",
            "8/8 [==============================] - 3s 347ms/step - loss: 6700.5791 - mean_absolute_percentage_error: 6700.5791 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 6563.6924 - val_mean_absolute_percentage_error: 6563.6924 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 18/50\n",
            "8/8 [==============================] - 3s 352ms/step - loss: 6442.2412 - mean_absolute_percentage_error: 6442.2412 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 6295.4492 - val_mean_absolute_percentage_error: 6295.4492 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 19/50\n",
            "8/8 [==============================] - 3s 350ms/step - loss: 6192.6787 - mean_absolute_percentage_error: 6192.6787 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 6057.1470 - val_mean_absolute_percentage_error: 6057.1470 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 20/50\n",
            "8/8 [==============================] - 3s 349ms/step - loss: 5951.3403 - mean_absolute_percentage_error: 5951.3403 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 5818.6992 - val_mean_absolute_percentage_error: 5818.6992 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 21/50\n",
            "8/8 [==============================] - 3s 350ms/step - loss: 5721.8618 - mean_absolute_percentage_error: 5721.8618 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 5625.0132 - val_mean_absolute_percentage_error: 5625.0132 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 22/50\n",
            "8/8 [==============================] - 3s 348ms/step - loss: 5534.8555 - mean_absolute_percentage_error: 5534.8555 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 5386.5659 - val_mean_absolute_percentage_error: 5386.5659 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 23/50\n",
            "8/8 [==============================] - 3s 352ms/step - loss: 5308.0801 - mean_absolute_percentage_error: 5308.0801 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 5205.8892 - val_mean_absolute_percentage_error: 5205.8892 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 24/50\n",
            "8/8 [==============================] - 3s 347ms/step - loss: 5120.9731 - mean_absolute_percentage_error: 5120.9731 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5001 - val_loss: 5027.1040 - val_mean_absolute_percentage_error: 5027.1040 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 25/50\n",
            "8/8 [==============================] - 3s 349ms/step - loss: 4942.1572 - mean_absolute_percentage_error: 4942.1572 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 4848.2900 - val_mean_absolute_percentage_error: 4848.2900 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 26/50\n",
            "8/8 [==============================] - 3s 348ms/step - loss: 4776.0195 - mean_absolute_percentage_error: 4776.0195 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 4654.5752 - val_mean_absolute_percentage_error: 4654.5752 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 27/50\n",
            "8/8 [==============================] - 3s 347ms/step - loss: 4590.5005 - mean_absolute_percentage_error: 4590.5005 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 4490.6333 - val_mean_absolute_percentage_error: 4490.6333 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 28/50\n",
            "8/8 [==============================] - 3s 348ms/step - loss: 4428.8198 - mean_absolute_percentage_error: 4428.8198 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 4326.7500 - val_mean_absolute_percentage_error: 4326.7500 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 29/50\n",
            "8/8 [==============================] - 3s 354ms/step - loss: 4272.3545 - mean_absolute_percentage_error: 4272.3545 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 4192.6392 - val_mean_absolute_percentage_error: 4192.6392 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 30/50\n",
            "8/8 [==============================] - 3s 349ms/step - loss: 4129.3096 - mean_absolute_percentage_error: 4129.3096 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 4058.4128 - val_mean_absolute_percentage_error: 4058.4128 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 31/50\n",
            "8/8 [==============================] - 3s 349ms/step - loss: 3996.5730 - mean_absolute_percentage_error: 3996.5730 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 3909.4016 - val_mean_absolute_percentage_error: 3909.4016 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 32/50\n",
            "8/8 [==============================] - 3s 356ms/step - loss: 3860.2273 - mean_absolute_percentage_error: 3860.2273 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 3775.2903 - val_mean_absolute_percentage_error: 3775.2903 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 33/50\n",
            "8/8 [==============================] - 4s 532ms/step - loss: 3728.3516 - mean_absolute_percentage_error: 3728.3521 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 3656.0813 - val_mean_absolute_percentage_error: 3656.0813 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 34/50\n",
            "8/8 [==============================] - 3s 349ms/step - loss: 3612.8677 - mean_absolute_percentage_error: 3612.8677 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 3536.8718 - val_mean_absolute_percentage_error: 3536.8718 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 35/50\n",
            "8/8 [==============================] - 3s 351ms/step - loss: 3483.2273 - mean_absolute_percentage_error: 3483.2273 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 3417.6626 - val_mean_absolute_percentage_error: 3417.6626 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 36/50\n",
            "8/8 [==============================] - 3s 350ms/step - loss: 3380.4097 - mean_absolute_percentage_error: 3380.4097 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 3298.4531 - val_mean_absolute_percentage_error: 3298.4531 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 37/50\n",
            "8/8 [==============================] - 3s 350ms/step - loss: 3270.1436 - mean_absolute_percentage_error: 3270.1436 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 3194.1523 - val_mean_absolute_percentage_error: 3194.1523 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 38/50\n",
            "8/8 [==============================] - 3s 349ms/step - loss: 3156.8997 - mean_absolute_percentage_error: 3156.8997 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 3119.6467 - val_mean_absolute_percentage_error: 3119.6467 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 39/50\n",
            "8/8 [==============================] - 3s 348ms/step - loss: 3065.2573 - mean_absolute_percentage_error: 3065.2573 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 3000.4373 - val_mean_absolute_percentage_error: 3000.4373 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 40/50\n",
            "8/8 [==============================] - 3s 352ms/step - loss: 2972.8696 - mean_absolute_percentage_error: 2972.8696 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 2925.9316 - val_mean_absolute_percentage_error: 2925.9316 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 41/50\n",
            "8/8 [==============================] - 3s 348ms/step - loss: 2871.5417 - mean_absolute_percentage_error: 2871.5417 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 2821.6216 - val_mean_absolute_percentage_error: 2821.6216 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 42/50\n",
            "8/8 [==============================] - 3s 344ms/step - loss: 2788.0952 - mean_absolute_percentage_error: 2788.0952 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 2747.1177 - val_mean_absolute_percentage_error: 2747.1177 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 43/50\n",
            "8/8 [==============================] - 3s 351ms/step - loss: 2705.3940 - mean_absolute_percentage_error: 2705.3940 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 2642.8096 - val_mean_absolute_percentage_error: 2642.8096 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 44/50\n",
            "8/8 [==============================] - 3s 349ms/step - loss: 2618.2217 - mean_absolute_percentage_error: 2618.2217 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 2583.2046 - val_mean_absolute_percentage_error: 2583.2046 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 45/50\n",
            "8/8 [==============================] - 3s 348ms/step - loss: 2541.4810 - mean_absolute_percentage_error: 2541.4810 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 2508.6987 - val_mean_absolute_percentage_error: 2508.6987 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 46/50\n",
            "8/8 [==============================] - 3s 351ms/step - loss: 2466.2302 - mean_absolute_percentage_error: 2466.2302 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 2419.2900 - val_mean_absolute_percentage_error: 2419.2900 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 47/50\n",
            "8/8 [==============================] - 3s 352ms/step - loss: 2390.9773 - mean_absolute_percentage_error: 2390.9773 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 2344.7788 - val_mean_absolute_percentage_error: 2344.7788 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 48/50\n",
            "8/8 [==============================] - 3s 350ms/step - loss: 2326.1526 - mean_absolute_percentage_error: 2326.1526 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 2285.1741 - val_mean_absolute_percentage_error: 2285.1741 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 49/50\n",
            "8/8 [==============================] - 3s 348ms/step - loss: 2250.9016 - mean_absolute_percentage_error: 2250.9016 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 2225.5696 - val_mean_absolute_percentage_error: 2225.5696 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 50/50\n",
            "8/8 [==============================] - 3s 352ms/step - loss: 2184.5913 - mean_absolute_percentage_error: 2184.5913 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 2165.9648 - val_mean_absolute_percentage_error: 2165.9648 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "2/2 [==============================] - 0s 78ms/step - loss: 2165.9648 - mean_absolute_percentage_error: 2165.9648 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000\n",
            "\n",
            "[cirq.SWAP, cirq.CNOT, cirq.CNOT]\n",
            "Model: \"sequential_60\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " pqc_60 (PQC)                (None, 1)                 8         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 8\n",
            "Trainable params: 8\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/50\n",
            "8/8 [==============================] - 3s 363ms/step - loss: 21424644.0000 - mean_absolute_percentage_error: 21424644.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.2877 - val_loss: 21279468.0000 - val_mean_absolute_percentage_error: 21279468.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4593\n",
            "Epoch 2/50\n",
            "8/8 [==============================] - 3s 348ms/step - loss: 21175528.0000 - mean_absolute_percentage_error: 21175528.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.2902 - val_loss: 21028320.0000 - val_mean_absolute_percentage_error: 21028320.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4597\n",
            "Epoch 3/50\n",
            "8/8 [==============================] - 3s 338ms/step - loss: 20927196.0000 - mean_absolute_percentage_error: 20927196.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.2927 - val_loss: 20776676.0000 - val_mean_absolute_percentage_error: 20776676.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4602\n",
            "Epoch 4/50\n",
            "8/8 [==============================] - 3s 339ms/step - loss: 20656196.0000 - mean_absolute_percentage_error: 20656196.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.2948 - val_loss: 20534668.0000 - val_mean_absolute_percentage_error: 20534668.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4606\n",
            "Epoch 5/50\n",
            "8/8 [==============================] - 3s 345ms/step - loss: 20407380.0000 - mean_absolute_percentage_error: 20407380.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.2972 - val_loss: 20292238.0000 - val_mean_absolute_percentage_error: 20292238.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4611\n",
            "Epoch 6/50\n",
            "8/8 [==============================] - 3s 338ms/step - loss: 20184532.0000 - mean_absolute_percentage_error: 20184532.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.2999 - val_loss: 20038406.0000 - val_mean_absolute_percentage_error: 20038406.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4615\n",
            "Epoch 7/50\n",
            "8/8 [==============================] - 3s 338ms/step - loss: 19932540.0000 - mean_absolute_percentage_error: 19932540.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3023 - val_loss: 19794528.0000 - val_mean_absolute_percentage_error: 19794528.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4620\n",
            "Epoch 8/50\n",
            "8/8 [==============================] - 3s 340ms/step - loss: 19685206.0000 - mean_absolute_percentage_error: 19685206.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3045 - val_loss: 19557826.0000 - val_mean_absolute_percentage_error: 19557826.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4624\n",
            "Epoch 9/50\n",
            "8/8 [==============================] - 3s 334ms/step - loss: 19455328.0000 - mean_absolute_percentage_error: 19455328.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3070 - val_loss: 19317620.0000 - val_mean_absolute_percentage_error: 19317620.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4629\n",
            "Epoch 10/50\n",
            "8/8 [==============================] - 3s 342ms/step - loss: 19218916.0000 - mean_absolute_percentage_error: 19218916.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3094 - val_loss: 19080054.0000 - val_mean_absolute_percentage_error: 19080054.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4633\n",
            "Epoch 11/50\n",
            "8/8 [==============================] - 4s 511ms/step - loss: 18975062.0000 - mean_absolute_percentage_error: 18975062.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3115 - val_loss: 18853588.0000 - val_mean_absolute_percentage_error: 18853588.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4637\n",
            "Epoch 12/50\n",
            "8/8 [==============================] - 3s 338ms/step - loss: 18744082.0000 - mean_absolute_percentage_error: 18744082.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3137 - val_loss: 18626976.0000 - val_mean_absolute_percentage_error: 18626976.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4641\n",
            "Epoch 13/50\n",
            "8/8 [==============================] - 3s 332ms/step - loss: 18530916.0000 - mean_absolute_percentage_error: 18530916.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3162 - val_loss: 18389046.0000 - val_mean_absolute_percentage_error: 18389046.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4646\n",
            "Epoch 14/50\n",
            "8/8 [==============================] - 3s 334ms/step - loss: 18291652.0000 - mean_absolute_percentage_error: 18291652.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3185 - val_loss: 18160272.0000 - val_mean_absolute_percentage_error: 18160272.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4650\n",
            "Epoch 15/50\n",
            "8/8 [==============================] - 3s 334ms/step - loss: 18059332.0000 - mean_absolute_percentage_error: 18059332.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3207 - val_loss: 17934726.0000 - val_mean_absolute_percentage_error: 17934726.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4654\n",
            "Epoch 16/50\n",
            "8/8 [==============================] - 3s 339ms/step - loss: 17840300.0000 - mean_absolute_percentage_error: 17840300.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3230 - val_loss: 17704940.0000 - val_mean_absolute_percentage_error: 17704940.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4658\n",
            "Epoch 17/50\n",
            "8/8 [==============================] - 3s 335ms/step - loss: 17609786.0000 - mean_absolute_percentage_error: 17609786.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3252 - val_loss: 17478992.0000 - val_mean_absolute_percentage_error: 17478992.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4663\n",
            "Epoch 18/50\n",
            "8/8 [==============================] - 3s 339ms/step - loss: 17377640.0000 - mean_absolute_percentage_error: 17377640.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3273 - val_loss: 17257724.0000 - val_mean_absolute_percentage_error: 17257724.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4667\n",
            "Epoch 19/50\n",
            "8/8 [==============================] - 3s 334ms/step - loss: 17168092.0000 - mean_absolute_percentage_error: 17168092.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3297 - val_loss: 17026492.0000 - val_mean_absolute_percentage_error: 17026492.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4671\n",
            "Epoch 20/50\n",
            "8/8 [==============================] - 3s 332ms/step - loss: 16925492.0000 - mean_absolute_percentage_error: 16925492.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3318 - val_loss: 16809676.0000 - val_mean_absolute_percentage_error: 16809676.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4675\n",
            "Epoch 21/50\n",
            "8/8 [==============================] - 3s 336ms/step - loss: 16707250.0000 - mean_absolute_percentage_error: 16707250.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3339 - val_loss: 16589818.0000 - val_mean_absolute_percentage_error: 16589818.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4679\n",
            "Epoch 22/50\n",
            "8/8 [==============================] - 3s 337ms/step - loss: 16489379.0000 - mean_absolute_percentage_error: 16489379.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3361 - val_loss: 16366477.0000 - val_mean_absolute_percentage_error: 16366477.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4683\n",
            "Epoch 23/50\n",
            "8/8 [==============================] - 3s 335ms/step - loss: 16265194.0000 - mean_absolute_percentage_error: 16265194.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3383 - val_loss: 16145707.0000 - val_mean_absolute_percentage_error: 16145707.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4688\n",
            "Epoch 24/50\n",
            "8/8 [==============================] - 3s 333ms/step - loss: 16060259.0000 - mean_absolute_percentage_error: 16060259.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3408 - val_loss: 15914856.0000 - val_mean_absolute_percentage_error: 15914856.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4692\n",
            "Epoch 25/50\n",
            "8/8 [==============================] - 3s 335ms/step - loss: 15812218.0000 - mean_absolute_percentage_error: 15812218.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3426 - val_loss: 15706210.0000 - val_mean_absolute_percentage_error: 15706210.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4696\n",
            "Epoch 26/50\n",
            "8/8 [==============================] - 3s 337ms/step - loss: 15621970.0000 - mean_absolute_percentage_error: 15621970.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3451 - val_loss: 15477045.0000 - val_mean_absolute_percentage_error: 15477045.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4700\n",
            "Epoch 27/50\n",
            "8/8 [==============================] - 3s 338ms/step - loss: 15369818.0000 - mean_absolute_percentage_error: 15369818.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3468 - val_loss: 15275515.0000 - val_mean_absolute_percentage_error: 15275515.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4704\n",
            "Epoch 28/50\n",
            "8/8 [==============================] - 3s 335ms/step - loss: 15169920.0000 - mean_absolute_percentage_error: 15169920.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3490 - val_loss: 15060187.0000 - val_mean_absolute_percentage_error: 15060187.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4708\n",
            "Epoch 29/50\n",
            "8/8 [==============================] - 3s 335ms/step - loss: 14970749.0000 - mean_absolute_percentage_error: 14970749.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3514 - val_loss: 14833434.0000 - val_mean_absolute_percentage_error: 14833434.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4712\n",
            "Epoch 30/50\n",
            "8/8 [==============================] - 3s 335ms/step - loss: 14744795.0000 - mean_absolute_percentage_error: 14744795.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3536 - val_loss: 14616021.0000 - val_mean_absolute_percentage_error: 14616021.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4716\n",
            "Epoch 31/50\n",
            "8/8 [==============================] - 3s 338ms/step - loss: 14524973.0000 - mean_absolute_percentage_error: 14524973.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3556 - val_loss: 14404389.0000 - val_mean_absolute_percentage_error: 14404389.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4720\n",
            "Epoch 32/50\n",
            "8/8 [==============================] - 3s 337ms/step - loss: 14305670.0000 - mean_absolute_percentage_error: 14305670.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3576 - val_loss: 14198493.0000 - val_mean_absolute_percentage_error: 14198493.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4724\n",
            "Epoch 33/50\n",
            "8/8 [==============================] - 3s 336ms/step - loss: 14093563.0000 - mean_absolute_percentage_error: 14093563.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3595 - val_loss: 13994606.0000 - val_mean_absolute_percentage_error: 13994606.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4728\n",
            "Epoch 34/50\n",
            "8/8 [==============================] - 3s 346ms/step - loss: 13909056.0000 - mean_absolute_percentage_error: 13909056.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3620 - val_loss: 13770640.0000 - val_mean_absolute_percentage_error: 13770640.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4732\n",
            "Epoch 35/50\n",
            "8/8 [==============================] - 3s 338ms/step - loss: 13677309.0000 - mean_absolute_percentage_error: 13677309.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3639 - val_loss: 13563741.0000 - val_mean_absolute_percentage_error: 13563741.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4736\n",
            "Epoch 36/50\n",
            "8/8 [==============================] - 3s 336ms/step - loss: 13469938.0000 - mean_absolute_percentage_error: 13469938.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3660 - val_loss: 13355637.0000 - val_mean_absolute_percentage_error: 13355637.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4740\n",
            "Epoch 37/50\n",
            "8/8 [==============================] - 3s 337ms/step - loss: 13266608.0000 - mean_absolute_percentage_error: 13266608.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3681 - val_loss: 13143917.0000 - val_mean_absolute_percentage_error: 13143917.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4744\n",
            "Epoch 38/50\n",
            "8/8 [==============================] - 3s 336ms/step - loss: 13048104.0000 - mean_absolute_percentage_error: 13048104.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3701 - val_loss: 12941105.0000 - val_mean_absolute_percentage_error: 12941105.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4748\n",
            "Epoch 39/50\n",
            "8/8 [==============================] - 3s 339ms/step - loss: 12850829.0000 - mean_absolute_percentage_error: 12850829.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3722 - val_loss: 12730853.0000 - val_mean_absolute_percentage_error: 12730853.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4752\n",
            "Epoch 40/50\n",
            "8/8 [==============================] - 3s 340ms/step - loss: 12645418.0000 - mean_absolute_percentage_error: 12645418.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3743 - val_loss: 12520444.0000 - val_mean_absolute_percentage_error: 12520444.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4756\n",
            "Epoch 41/50\n",
            "8/8 [==============================] - 3s 336ms/step - loss: 12433844.0000 - mean_absolute_percentage_error: 12433844.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3764 - val_loss: 12314728.0000 - val_mean_absolute_percentage_error: 12314728.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4760\n",
            "Epoch 42/50\n",
            "8/8 [==============================] - 3s 381ms/step - loss: 12241140.0000 - mean_absolute_percentage_error: 12241140.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3786 - val_loss: 12101256.0000 - val_mean_absolute_percentage_error: 12101256.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4764\n",
            "Epoch 43/50\n",
            "8/8 [==============================] - 3s 421ms/step - loss: 12015542.0000 - mean_absolute_percentage_error: 12015542.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3804 - val_loss: 11903665.0000 - val_mean_absolute_percentage_error: 11903665.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4768\n",
            "Epoch 44/50\n",
            "8/8 [==============================] - 3s 339ms/step - loss: 11822386.0000 - mean_absolute_percentage_error: 11822386.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3825 - val_loss: 11698677.0000 - val_mean_absolute_percentage_error: 11698677.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4772\n",
            "Epoch 45/50\n",
            "8/8 [==============================] - 3s 337ms/step - loss: 11616621.0000 - mean_absolute_percentage_error: 11616621.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3845 - val_loss: 11496695.0000 - val_mean_absolute_percentage_error: 11496695.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4775\n",
            "Epoch 46/50\n",
            "8/8 [==============================] - 3s 340ms/step - loss: 11395269.0000 - mean_absolute_percentage_error: 11395269.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3861 - val_loss: 11311943.0000 - val_mean_absolute_percentage_error: 11311943.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4779\n",
            "Epoch 47/50\n",
            "8/8 [==============================] - 3s 335ms/step - loss: 11227628.0000 - mean_absolute_percentage_error: 11227627.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3884 - val_loss: 11100364.0000 - val_mean_absolute_percentage_error: 11100364.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4783\n",
            "Epoch 48/50\n",
            "8/8 [==============================] - 3s 339ms/step - loss: 11010027.0000 - mean_absolute_percentage_error: 11010026.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3903 - val_loss: 10902626.0000 - val_mean_absolute_percentage_error: 10902626.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4787\n",
            "Epoch 49/50\n",
            "8/8 [==============================] - 3s 337ms/step - loss: 10809622.0000 - mean_absolute_percentage_error: 10809622.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3922 - val_loss: 10705715.0000 - val_mean_absolute_percentage_error: 10705715.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4790\n",
            "Epoch 50/50\n",
            "8/8 [==============================] - 3s 341ms/step - loss: 10621055.0000 - mean_absolute_percentage_error: 10621055.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3943 - val_loss: 10501518.0000 - val_mean_absolute_percentage_error: 10501518.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4794\n",
            "2/2 [==============================] - 1s 93ms/step - loss: 10501518.0000 - mean_absolute_percentage_error: 10501518.0000 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4794\n",
            "\n",
            "[cirq.SWAP, cirq.CNOT, cirq.CNOT]\n",
            "Model: \"sequential_61\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " pqc_61 (PQC)                (None, 1)                 32        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 32\n",
            "Trainable params: 32\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/50\n",
            "8/8 [==============================] - 6s 757ms/step - loss: 10657646.0000 - mean_absolute_percentage_error: 10657646.0000 - mean_absolute_error: 2.5213 - mean_squared_error: 12.6069 - val_loss: 10526509.0000 - val_mean_absolute_percentage_error: 10526509.0000 - val_mean_absolute_error: 0.5211 - val_mean_squared_error: 0.5215\n",
            "Epoch 2/50\n",
            "8/8 [==============================] - 6s 734ms/step - loss: 10426289.0000 - mean_absolute_percentage_error: 10426289.0000 - mean_absolute_error: 2.5209 - mean_squared_error: 12.6048 - val_loss: 10310925.0000 - val_mean_absolute_percentage_error: 10310925.0000 - val_mean_absolute_error: 0.5206 - val_mean_squared_error: 0.5210\n",
            "Epoch 3/50\n",
            "8/8 [==============================] - 6s 732ms/step - loss: 10210297.0000 - mean_absolute_percentage_error: 10210297.0000 - mean_absolute_error: 2.5204 - mean_squared_error: 12.6027 - val_loss: 10094276.0000 - val_mean_absolute_percentage_error: 10094276.0000 - val_mean_absolute_error: 0.5202 - val_mean_squared_error: 0.5206\n",
            "Epoch 4/50\n",
            "8/8 [==============================] - 6s 728ms/step - loss: 9988630.0000 - mean_absolute_percentage_error: 9988630.0000 - mean_absolute_error: 2.5200 - mean_squared_error: 12.6005 - val_loss: 9875702.0000 - val_mean_absolute_percentage_error: 9875702.0000 - val_mean_absolute_error: 0.5198 - val_mean_squared_error: 0.5201\n",
            "Epoch 5/50\n",
            "8/8 [==============================] - 6s 734ms/step - loss: 9771978.0000 - mean_absolute_percentage_error: 9771978.0000 - mean_absolute_error: 2.5196 - mean_squared_error: 12.5982 - val_loss: 9652823.0000 - val_mean_absolute_percentage_error: 9652823.0000 - val_mean_absolute_error: 0.5193 - val_mean_squared_error: 0.5197\n",
            "Epoch 6/50\n",
            "8/8 [==============================] - 6s 734ms/step - loss: 9544028.0000 - mean_absolute_percentage_error: 9544028.0000 - mean_absolute_error: 2.5191 - mean_squared_error: 12.5961 - val_loss: 9434548.0000 - val_mean_absolute_percentage_error: 9434548.0000 - val_mean_absolute_error: 0.5189 - val_mean_squared_error: 0.5192\n",
            "Epoch 7/50\n",
            "8/8 [==============================] - 6s 736ms/step - loss: 9345452.0000 - mean_absolute_percentage_error: 9345452.0000 - mean_absolute_error: 2.5187 - mean_squared_error: 12.5935 - val_loss: 9202712.0000 - val_mean_absolute_percentage_error: 9202712.0000 - val_mean_absolute_error: 0.5184 - val_mean_squared_error: 0.5187\n",
            "Epoch 8/50\n",
            "8/8 [==============================] - 6s 740ms/step - loss: 9112933.0000 - mean_absolute_percentage_error: 9112933.0000 - mean_absolute_error: 2.5182 - mean_squared_error: 12.5913 - val_loss: 8979434.0000 - val_mean_absolute_percentage_error: 8979434.0000 - val_mean_absolute_error: 0.5180 - val_mean_squared_error: 0.5183\n",
            "Epoch 9/50\n",
            "8/8 [==============================] - 6s 741ms/step - loss: 8890721.0000 - mean_absolute_percentage_error: 8890721.0000 - mean_absolute_error: 2.5178 - mean_squared_error: 12.5890 - val_loss: 8754724.0000 - val_mean_absolute_percentage_error: 8754724.0000 - val_mean_absolute_error: 0.5175 - val_mean_squared_error: 0.5178\n",
            "Epoch 10/50\n",
            "8/8 [==============================] - 6s 743ms/step - loss: 8669178.0000 - mean_absolute_percentage_error: 8669178.0000 - mean_absolute_error: 2.5173 - mean_squared_error: 12.5867 - val_loss: 8526644.0000 - val_mean_absolute_percentage_error: 8526644.0000 - val_mean_absolute_error: 0.5171 - val_mean_squared_error: 0.5173\n",
            "Epoch 11/50\n",
            "8/8 [==============================] - 7s 896ms/step - loss: 8445245.0000 - mean_absolute_percentage_error: 8445245.0000 - mean_absolute_error: 2.5168 - mean_squared_error: 12.5843 - val_loss: 8296021.5000 - val_mean_absolute_percentage_error: 8296021.5000 - val_mean_absolute_error: 0.5166 - val_mean_squared_error: 0.5169\n",
            "Epoch 12/50\n",
            "8/8 [==============================] - 6s 736ms/step - loss: 8196026.5000 - mean_absolute_percentage_error: 8196026.5000 - mean_absolute_error: 2.5164 - mean_squared_error: 12.5824 - val_loss: 8077096.0000 - val_mean_absolute_percentage_error: 8077096.0000 - val_mean_absolute_error: 0.5162 - val_mean_squared_error: 0.5164\n",
            "Epoch 13/50\n",
            "8/8 [==============================] - 6s 739ms/step - loss: 7975405.0000 - mean_absolute_percentage_error: 7975405.0000 - mean_absolute_error: 2.5160 - mean_squared_error: 12.5800 - val_loss: 7845472.0000 - val_mean_absolute_percentage_error: 7845472.0000 - val_mean_absolute_error: 0.5157 - val_mean_squared_error: 0.5159\n",
            "Epoch 14/50\n",
            "8/8 [==============================] - 6s 740ms/step - loss: 7752898.5000 - mean_absolute_percentage_error: 7752898.5000 - mean_absolute_error: 2.5155 - mean_squared_error: 12.5775 - val_loss: 7603424.0000 - val_mean_absolute_percentage_error: 7603424.0000 - val_mean_absolute_error: 0.5152 - val_mean_squared_error: 0.5154\n",
            "Epoch 15/50\n",
            "8/8 [==============================] - 6s 734ms/step - loss: 7501618.5000 - mean_absolute_percentage_error: 7501618.5000 - mean_absolute_error: 2.5150 - mean_squared_error: 12.5752 - val_loss: 7368798.5000 - val_mean_absolute_percentage_error: 7368798.5000 - val_mean_absolute_error: 0.5147 - val_mean_squared_error: 0.5150\n",
            "Epoch 16/50\n",
            "8/8 [==============================] - 6s 739ms/step - loss: 7269254.5000 - mean_absolute_percentage_error: 7269254.5000 - mean_absolute_error: 2.5145 - mean_squared_error: 12.5727 - val_loss: 7125494.5000 - val_mean_absolute_percentage_error: 7125494.5000 - val_mean_absolute_error: 0.5143 - val_mean_squared_error: 0.5145\n",
            "Epoch 17/50\n",
            "8/8 [==============================] - 6s 734ms/step - loss: 7032442.5000 - mean_absolute_percentage_error: 7032442.5000 - mean_absolute_error: 2.5140 - mean_squared_error: 12.5702 - val_loss: 6876692.0000 - val_mean_absolute_percentage_error: 6876692.0000 - val_mean_absolute_error: 0.5138 - val_mean_squared_error: 0.5139\n",
            "Epoch 18/50\n",
            "8/8 [==============================] - 6s 738ms/step - loss: 6770133.0000 - mean_absolute_percentage_error: 6770133.0000 - mean_absolute_error: 2.5135 - mean_squared_error: 12.5679 - val_loss: 6638631.0000 - val_mean_absolute_percentage_error: 6638631.0000 - val_mean_absolute_error: 0.5133 - val_mean_squared_error: 0.5135\n",
            "Epoch 19/50\n",
            "8/8 [==============================] - 6s 737ms/step - loss: 6526062.5000 - mean_absolute_percentage_error: 6526062.5000 - mean_absolute_error: 2.5131 - mean_squared_error: 12.5655 - val_loss: 6395086.5000 - val_mean_absolute_percentage_error: 6395086.5000 - val_mean_absolute_error: 0.5128 - val_mean_squared_error: 0.5130\n",
            "Epoch 20/50\n",
            "8/8 [==============================] - 6s 746ms/step - loss: 6286628.0000 - mean_absolute_percentage_error: 6286628.0000 - mean_absolute_error: 2.5126 - mean_squared_error: 12.5630 - val_loss: 6142168.0000 - val_mean_absolute_percentage_error: 6142168.0000 - val_mean_absolute_error: 0.5123 - val_mean_squared_error: 0.5124\n",
            "Epoch 21/50\n",
            "8/8 [==============================] - 6s 742ms/step - loss: 6039390.5000 - mean_absolute_percentage_error: 6039390.5000 - mean_absolute_error: 2.5121 - mean_squared_error: 12.5603 - val_loss: 5884565.5000 - val_mean_absolute_percentage_error: 5884565.5000 - val_mean_absolute_error: 0.5118 - val_mean_squared_error: 0.5119\n",
            "Epoch 22/50\n",
            "8/8 [==============================] - 6s 746ms/step - loss: 5775484.0000 - mean_absolute_percentage_error: 5775484.0000 - mean_absolute_error: 2.5116 - mean_squared_error: 12.5579 - val_loss: 5634061.5000 - val_mean_absolute_percentage_error: 5634061.5000 - val_mean_absolute_error: 0.5113 - val_mean_squared_error: 0.5114\n",
            "Epoch 23/50\n",
            "8/8 [==============================] - 6s 742ms/step - loss: 5529697.0000 - mean_absolute_percentage_error: 5529697.0000 - mean_absolute_error: 2.5110 - mean_squared_error: 12.5553 - val_loss: 5377513.5000 - val_mean_absolute_percentage_error: 5377513.5000 - val_mean_absolute_error: 0.5108 - val_mean_squared_error: 0.5109\n",
            "Epoch 24/50\n",
            "8/8 [==============================] - 6s 746ms/step - loss: 5277392.0000 - mean_absolute_percentage_error: 5277392.0000 - mean_absolute_error: 2.5105 - mean_squared_error: 12.5526 - val_loss: 5119227.0000 - val_mean_absolute_percentage_error: 5119227.0000 - val_mean_absolute_error: 0.5102 - val_mean_squared_error: 0.5103\n",
            "Epoch 25/50\n",
            "8/8 [==============================] - 7s 920ms/step - loss: 4998409.5000 - mean_absolute_percentage_error: 4998409.5000 - mean_absolute_error: 2.5100 - mean_squared_error: 12.5504 - val_loss: 4877829.0000 - val_mean_absolute_percentage_error: 4877829.0000 - val_mean_absolute_error: 0.5098 - val_mean_squared_error: 0.5099\n",
            "Epoch 26/50\n",
            "8/8 [==============================] - 6s 739ms/step - loss: 4770250.0000 - mean_absolute_percentage_error: 4770250.0000 - mean_absolute_error: 2.5095 - mean_squared_error: 12.5476 - val_loss: 4613439.5000 - val_mean_absolute_percentage_error: 4613439.5000 - val_mean_absolute_error: 0.5092 - val_mean_squared_error: 0.5093\n",
            "Epoch 27/50\n",
            "8/8 [==============================] - 6s 737ms/step - loss: 4493665.0000 - mean_absolute_percentage_error: 4493665.0000 - mean_absolute_error: 2.5090 - mean_squared_error: 12.5452 - val_loss: 4361615.0000 - val_mean_absolute_percentage_error: 4361615.0000 - val_mean_absolute_error: 0.5087 - val_mean_squared_error: 0.5088\n",
            "Epoch 28/50\n",
            "8/8 [==============================] - 6s 745ms/step - loss: 4240465.5000 - mean_absolute_percentage_error: 4240465.5000 - mean_absolute_error: 2.5085 - mean_squared_error: 12.5427 - val_loss: 4103291.5000 - val_mean_absolute_percentage_error: 4103291.5000 - val_mean_absolute_error: 0.5082 - val_mean_squared_error: 0.5083\n",
            "Epoch 29/50\n",
            "8/8 [==============================] - 6s 740ms/step - loss: 3989835.2500 - mean_absolute_percentage_error: 3989835.2500 - mean_absolute_error: 2.5080 - mean_squared_error: 12.5399 - val_loss: 3836947.5000 - val_mean_absolute_percentage_error: 3836947.5000 - val_mean_absolute_error: 0.5077 - val_mean_squared_error: 0.5077\n",
            "Epoch 30/50\n",
            "8/8 [==============================] - 6s 738ms/step - loss: 3720101.5000 - mean_absolute_percentage_error: 3720101.5000 - mean_absolute_error: 2.5075 - mean_squared_error: 12.5374 - val_loss: 3577454.5000 - val_mean_absolute_percentage_error: 3577454.5000 - val_mean_absolute_error: 0.5072 - val_mean_squared_error: 0.5072\n",
            "Epoch 31/50\n",
            "8/8 [==============================] - 6s 744ms/step - loss: 3469467.2500 - mean_absolute_percentage_error: 3469467.2500 - mean_absolute_error: 2.5069 - mean_squared_error: 12.5345 - val_loss: 3308371.2500 - val_mean_absolute_percentage_error: 3308371.2500 - val_mean_absolute_error: 0.5066 - val_mean_squared_error: 0.5067\n",
            "Epoch 32/50\n",
            "8/8 [==============================] - 6s 745ms/step - loss: 3204421.5000 - mean_absolute_percentage_error: 3204421.5000 - mean_absolute_error: 2.5064 - mean_squared_error: 12.5318 - val_loss: 3040233.5000 - val_mean_absolute_percentage_error: 3040233.5000 - val_mean_absolute_error: 0.5061 - val_mean_squared_error: 0.5061\n",
            "Epoch 33/50\n",
            "8/8 [==============================] - 6s 740ms/step - loss: 2922917.2500 - mean_absolute_percentage_error: 2922917.2500 - mean_absolute_error: 2.5059 - mean_squared_error: 12.5294 - val_loss: 2783750.5000 - val_mean_absolute_percentage_error: 2783750.5000 - val_mean_absolute_error: 0.5056 - val_mean_squared_error: 0.5056\n",
            "Epoch 34/50\n",
            "8/8 [==============================] - 6s 738ms/step - loss: 2663989.7500 - mean_absolute_percentage_error: 2663989.5000 - mean_absolute_error: 2.5053 - mean_squared_error: 12.5268 - val_loss: 2520664.7500 - val_mean_absolute_percentage_error: 2520664.7500 - val_mean_absolute_error: 0.5050 - val_mean_squared_error: 0.5051\n",
            "Epoch 35/50\n",
            "8/8 [==============================] - 6s 737ms/step - loss: 2400645.0000 - mean_absolute_percentage_error: 2400645.0000 - mean_absolute_error: 2.5048 - mean_squared_error: 12.5241 - val_loss: 2253565.2500 - val_mean_absolute_percentage_error: 2253565.2500 - val_mean_absolute_error: 0.5045 - val_mean_squared_error: 0.5045\n",
            "Epoch 36/50\n",
            "8/8 [==============================] - 6s 745ms/step - loss: 2130190.0000 - mean_absolute_percentage_error: 2130190.0000 - mean_absolute_error: 2.5043 - mean_squared_error: 12.5215 - val_loss: 1986740.3750 - val_mean_absolute_percentage_error: 1986740.3750 - val_mean_absolute_error: 0.5040 - val_mean_squared_error: 0.5040\n",
            "Epoch 37/50\n",
            "8/8 [==============================] - 6s 739ms/step - loss: 1867427.3750 - mean_absolute_percentage_error: 1867427.3750 - mean_absolute_error: 2.5037 - mean_squared_error: 12.5187 - val_loss: 1713093.0000 - val_mean_absolute_percentage_error: 1713093.0000 - val_mean_absolute_error: 0.5034 - val_mean_squared_error: 0.5034\n",
            "Epoch 38/50\n",
            "8/8 [==============================] - 6s 738ms/step - loss: 1611402.5000 - mean_absolute_percentage_error: 1611402.5000 - mean_absolute_error: 2.5032 - mean_squared_error: 12.5156 - val_loss: 1429046.7500 - val_mean_absolute_percentage_error: 1429046.7500 - val_mean_absolute_error: 0.5029 - val_mean_squared_error: 0.5029\n",
            "Epoch 39/50\n",
            "8/8 [==============================] - 7s 927ms/step - loss: 1322282.5000 - mean_absolute_percentage_error: 1322282.5000 - mean_absolute_error: 2.5026 - mean_squared_error: 12.5130 - val_loss: 1157741.6250 - val_mean_absolute_percentage_error: 1157741.6250 - val_mean_absolute_error: 0.5023 - val_mean_squared_error: 0.5023\n",
            "Epoch 40/50\n",
            "8/8 [==============================] - 6s 738ms/step - loss: 1039247.3125 - mean_absolute_percentage_error: 1039247.3125 - mean_absolute_error: 2.5021 - mean_squared_error: 12.5105 - val_loss: 894695.8750 - val_mean_absolute_percentage_error: 894695.8750 - val_mean_absolute_error: 0.5018 - val_mean_squared_error: 0.5018\n",
            "Epoch 41/50\n",
            "8/8 [==============================] - 6s 741ms/step - loss: 789226.3125 - mean_absolute_percentage_error: 789226.3125 - mean_absolute_error: 2.5015 - mean_squared_error: 12.5075 - val_loss: 614138.3125 - val_mean_absolute_percentage_error: 614138.3125 - val_mean_absolute_error: 0.5012 - val_mean_squared_error: 0.5012\n",
            "Epoch 42/50\n",
            "8/8 [==============================] - 6s 739ms/step - loss: 522951.5938 - mean_absolute_percentage_error: 522951.5938 - mean_absolute_error: 2.5010 - mean_squared_error: 12.5045 - val_loss: 330841.1875 - val_mean_absolute_percentage_error: 330841.1875 - val_mean_absolute_error: 0.5007 - val_mean_squared_error: 0.5007\n",
            "Epoch 43/50\n",
            "8/8 [==============================] - 6s 741ms/step - loss: 220128.2812 - mean_absolute_percentage_error: 220128.2812 - mean_absolute_error: 2.5004 - mean_squared_error: 12.5022 - val_loss: 67933.8281 - val_mean_absolute_percentage_error: 67933.8281 - val_mean_absolute_error: 0.5001 - val_mean_squared_error: 0.5001\n",
            "Epoch 44/50\n",
            "8/8 [==============================] - 6s 736ms/step - loss: 65931.4219 - mean_absolute_percentage_error: 65931.4219 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4997 - val_loss: 118970.5234 - val_mean_absolute_percentage_error: 118970.5234 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4998\n",
            "Epoch 45/50\n",
            "8/8 [==============================] - 6s 734ms/step - loss: 104610.7031 - mean_absolute_percentage_error: 104610.7031 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4991 - val_loss: 43225.3711 - val_mean_absolute_percentage_error: 43225.3711 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4999\n",
            "Epoch 46/50\n",
            "8/8 [==============================] - 6s 737ms/step - loss: 26118.1191 - mean_absolute_percentage_error: 26118.1191 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 31721.9961 - val_mean_absolute_percentage_error: 31721.9961 - val_mean_absolute_error: 0.5001 - val_mean_squared_error: 0.5001\n",
            "Epoch 47/50\n",
            "8/8 [==============================] - 6s 736ms/step - loss: 16877.2344 - mean_absolute_percentage_error: 16877.2344 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 21233.2207 - val_mean_absolute_percentage_error: 21233.2207 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 48/50\n",
            "8/8 [==============================] - 6s 735ms/step - loss: 14297.8223 - mean_absolute_percentage_error: 14297.8223 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 26081.1641 - val_mean_absolute_percentage_error: 26081.1641 - val_mean_absolute_error: 0.5001 - val_mean_squared_error: 0.5001\n",
            "Epoch 49/50\n",
            "8/8 [==============================] - 6s 738ms/step - loss: 15116.6123 - mean_absolute_percentage_error: 15116.6123 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 4592.6948 - val_mean_absolute_percentage_error: 4592.6948 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 50/50\n",
            "8/8 [==============================] - 6s 736ms/step - loss: 2346.1467 - mean_absolute_percentage_error: 2346.1465 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 3212.1465 - val_mean_absolute_percentage_error: 3212.1465 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "2/2 [==============================] - 1s 156ms/step - loss: 3212.1460 - mean_absolute_percentage_error: 3212.1460 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000\n",
            "\n",
            "[cirq.SWAP, cirq.ZZ, cirq.CZ, cirq.SWAP]\n",
            "Model: \"sequential_62\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " pqc_62 (PQC)                (None, 1)                 12        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 12\n",
            "Trainable params: 12\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/50\n",
            "8/8 [==============================] - 4s 423ms/step - loss: 216790144.0000 - mean_absolute_percentage_error: 216790176.0000 - mean_absolute_error: 2.5005 - mean_squared_error: 10.5244 - val_loss: 215108832.0000 - val_mean_absolute_percentage_error: 215108832.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2549\n",
            "Epoch 2/50\n",
            "8/8 [==============================] - 3s 393ms/step - loss: 213919696.0000 - mean_absolute_percentage_error: 213919696.0000 - mean_absolute_error: 2.4998 - mean_squared_error: 10.5416 - val_loss: 212601296.0000 - val_mean_absolute_percentage_error: 212601296.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2556\n",
            "Epoch 3/50\n",
            "8/8 [==============================] - 3s 394ms/step - loss: 211581792.0000 - mean_absolute_percentage_error: 211581792.0000 - mean_absolute_error: 2.5003 - mean_squared_error: 10.5664 - val_loss: 209911552.0000 - val_mean_absolute_percentage_error: 209911552.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2564\n",
            "Epoch 4/50\n",
            "8/8 [==============================] - 3s 389ms/step - loss: 208792304.0000 - mean_absolute_percentage_error: 208792304.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 10.5871 - val_loss: 207257888.0000 - val_mean_absolute_percentage_error: 207257888.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2573\n",
            "Epoch 5/50\n",
            "8/8 [==============================] - 3s 393ms/step - loss: 206133408.0000 - mean_absolute_percentage_error: 206133408.0000 - mean_absolute_error: 2.5002 - mean_squared_error: 10.6102 - val_loss: 204495648.0000 - val_mean_absolute_percentage_error: 204495648.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2583\n",
            "Epoch 6/50\n",
            "8/8 [==============================] - 3s 392ms/step - loss: 203339232.0000 - mean_absolute_percentage_error: 203339232.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 10.6333 - val_loss: 201684656.0000 - val_mean_absolute_percentage_error: 201684656.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2593\n",
            "Epoch 7/50\n",
            "8/8 [==============================] - 3s 390ms/step - loss: 200370080.0000 - mean_absolute_percentage_error: 200370080.0000 - mean_absolute_error: 2.4998 - mean_squared_error: 10.6547 - val_loss: 198909280.0000 - val_mean_absolute_percentage_error: 198909280.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2604\n",
            "Epoch 8/50\n",
            "8/8 [==============================] - 3s 396ms/step - loss: 197730464.0000 - mean_absolute_percentage_error: 197730464.0000 - mean_absolute_error: 2.5003 - mean_squared_error: 10.6816 - val_loss: 195957840.0000 - val_mean_absolute_percentage_error: 195957840.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2617\n",
            "Epoch 9/50\n",
            "8/8 [==============================] - 3s 396ms/step - loss: 194815392.0000 - mean_absolute_percentage_error: 194815392.0000 - mean_absolute_error: 2.5003 - mean_squared_error: 10.7068 - val_loss: 192989184.0000 - val_mean_absolute_percentage_error: 192989184.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2630\n",
            "Epoch 10/50\n",
            "8/8 [==============================] - 3s 394ms/step - loss: 191740928.0000 - mean_absolute_percentage_error: 191740928.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 10.7300 - val_loss: 190071728.0000 - val_mean_absolute_percentage_error: 190071728.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2644\n",
            "Epoch 11/50\n",
            "8/8 [==============================] - 3s 396ms/step - loss: 188675328.0000 - mean_absolute_percentage_error: 188675328.0000 - mean_absolute_error: 2.4997 - mean_squared_error: 10.7525 - val_loss: 187178448.0000 - val_mean_absolute_percentage_error: 187178448.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2658\n",
            "Epoch 12/50\n",
            "8/8 [==============================] - 3s 393ms/step - loss: 185888096.0000 - mean_absolute_percentage_error: 185888096.0000 - mean_absolute_error: 2.5002 - mean_squared_error: 10.7808 - val_loss: 184082592.0000 - val_mean_absolute_percentage_error: 184082592.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2674\n",
            "Epoch 13/50\n",
            "8/8 [==============================] - 3s 391ms/step - loss: 182914592.0000 - mean_absolute_percentage_error: 182914592.0000 - mean_absolute_error: 2.5005 - mean_squared_error: 10.8089 - val_loss: 180926080.0000 - val_mean_absolute_percentage_error: 180926080.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2691\n",
            "Epoch 14/50\n",
            "8/8 [==============================] - 3s 394ms/step - loss: 179438704.0000 - mean_absolute_percentage_error: 179438704.0000 - mean_absolute_error: 2.4994 - mean_squared_error: 10.8290 - val_loss: 178058448.0000 - val_mean_absolute_percentage_error: 178058448.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2707\n",
            "Epoch 15/50\n",
            "8/8 [==============================] - 3s 391ms/step - loss: 176826464.0000 - mean_absolute_percentage_error: 176826464.0000 - mean_absolute_error: 2.5005 - mean_squared_error: 10.8611 - val_loss: 174788816.0000 - val_mean_absolute_percentage_error: 174788816.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2726\n",
            "Epoch 16/50\n",
            "8/8 [==============================] - 3s 394ms/step - loss: 173367968.0000 - mean_absolute_percentage_error: 173367968.0000 - mean_absolute_error: 2.4998 - mean_squared_error: 10.8843 - val_loss: 171729008.0000 - val_mean_absolute_percentage_error: 171729008.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2745\n",
            "Epoch 17/50\n",
            "8/8 [==============================] - 3s 393ms/step - loss: 170274992.0000 - mean_absolute_percentage_error: 170274992.0000 - mean_absolute_error: 2.4998 - mean_squared_error: 10.9118 - val_loss: 168570480.0000 - val_mean_absolute_percentage_error: 168570480.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2765\n",
            "Epoch 18/50\n",
            "8/8 [==============================] - 3s 394ms/step - loss: 167087280.0000 - mean_absolute_percentage_error: 167087280.0000 - mean_absolute_error: 2.4998 - mean_squared_error: 10.9390 - val_loss: 165382608.0000 - val_mean_absolute_percentage_error: 165382608.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2786\n",
            "Epoch 19/50\n",
            "8/8 [==============================] - 3s 394ms/step - loss: 164015296.0000 - mean_absolute_percentage_error: 164015296.0000 - mean_absolute_error: 2.5002 - mean_squared_error: 10.9695 - val_loss: 162070304.0000 - val_mean_absolute_percentage_error: 162070304.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2809\n",
            "Epoch 20/50\n",
            "8/8 [==============================] - 3s 394ms/step - loss: 160798432.0000 - mean_absolute_percentage_error: 160798432.0000 - mean_absolute_error: 2.5004 - mean_squared_error: 10.9994 - val_loss: 158713840.0000 - val_mean_absolute_percentage_error: 158713840.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2833\n",
            "Epoch 21/50\n",
            "8/8 [==============================] - 4s 579ms/step - loss: 157147488.0000 - mean_absolute_percentage_error: 157147472.0000 - mean_absolute_error: 2.4995 - mean_squared_error: 11.0224 - val_loss: 155631184.0000 - val_mean_absolute_percentage_error: 155631184.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2856\n",
            "Epoch 22/50\n",
            "8/8 [==============================] - 3s 391ms/step - loss: 154058544.0000 - mean_absolute_percentage_error: 154058544.0000 - mean_absolute_error: 2.4997 - mean_squared_error: 11.0514 - val_loss: 152348144.0000 - val_mean_absolute_percentage_error: 152348144.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2881\n",
            "Epoch 23/50\n",
            "8/8 [==============================] - 3s 395ms/step - loss: 150864624.0000 - mean_absolute_percentage_error: 150864624.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.0828 - val_loss: 148938672.0000 - val_mean_absolute_percentage_error: 148938672.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2909\n",
            "Epoch 24/50\n",
            "8/8 [==============================] - 3s 396ms/step - loss: 147324576.0000 - mean_absolute_percentage_error: 147324576.0000 - mean_absolute_error: 2.4996 - mean_squared_error: 11.1102 - val_loss: 145649312.0000 - val_mean_absolute_percentage_error: 145649312.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2936\n",
            "Epoch 25/50\n",
            "8/8 [==============================] - 3s 399ms/step - loss: 144171744.0000 - mean_absolute_percentage_error: 144171744.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 11.1420 - val_loss: 142171424.0000 - val_mean_absolute_percentage_error: 142171424.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2965\n",
            "Epoch 26/50\n",
            "8/8 [==============================] - 3s 395ms/step - loss: 140604576.0000 - mean_absolute_percentage_error: 140604576.0000 - mean_absolute_error: 2.4998 - mean_squared_error: 11.1711 - val_loss: 138809056.0000 - val_mean_absolute_percentage_error: 138809056.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.2995\n",
            "Epoch 27/50\n",
            "8/8 [==============================] - 3s 394ms/step - loss: 137288368.0000 - mean_absolute_percentage_error: 137288368.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 11.2025 - val_loss: 135350656.0000 - val_mean_absolute_percentage_error: 135350656.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3026\n",
            "Epoch 28/50\n",
            "8/8 [==============================] - 3s 400ms/step - loss: 133896104.0000 - mean_absolute_percentage_error: 133896104.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 11.2341 - val_loss: 131838744.0000 - val_mean_absolute_percentage_error: 131838744.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3058\n",
            "Epoch 29/50\n",
            "8/8 [==============================] - 3s 397ms/step - loss: 130376232.0000 - mean_absolute_percentage_error: 130376240.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 11.2651 - val_loss: 128353560.0000 - val_mean_absolute_percentage_error: 128353560.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3092\n",
            "Epoch 30/50\n",
            "8/8 [==============================] - 3s 397ms/step - loss: 126708800.0000 - mean_absolute_percentage_error: 126708816.0000 - mean_absolute_error: 2.4996 - mean_squared_error: 11.2937 - val_loss: 125003888.0000 - val_mean_absolute_percentage_error: 125003888.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3125\n",
            "Epoch 31/50\n",
            "8/8 [==============================] - 3s 398ms/step - loss: 123451200.0000 - mean_absolute_percentage_error: 123451200.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 11.3269 - val_loss: 121442512.0000 - val_mean_absolute_percentage_error: 121442512.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3161\n",
            "Epoch 32/50\n",
            "8/8 [==============================] - 3s 392ms/step - loss: 119941488.0000 - mean_absolute_percentage_error: 119941488.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 11.3595 - val_loss: 117858160.0000 - val_mean_absolute_percentage_error: 117858160.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3198\n",
            "Epoch 33/50\n",
            "8/8 [==============================] - 3s 392ms/step - loss: 116377576.0000 - mean_absolute_percentage_error: 116377576.0000 - mean_absolute_error: 2.5002 - mean_squared_error: 11.3922 - val_loss: 114290496.0000 - val_mean_absolute_percentage_error: 114290496.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3237\n",
            "Epoch 34/50\n",
            "8/8 [==============================] - 3s 394ms/step - loss: 112659760.0000 - mean_absolute_percentage_error: 112659760.0000 - mean_absolute_error: 2.4997 - mean_squared_error: 11.4211 - val_loss: 110848792.0000 - val_mean_absolute_percentage_error: 110848792.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3275\n",
            "Epoch 35/50\n",
            "8/8 [==============================] - 3s 398ms/step - loss: 109247792.0000 - mean_absolute_percentage_error: 109247792.0000 - mean_absolute_error: 2.4999 - mean_squared_error: 11.4541 - val_loss: 107267832.0000 - val_mean_absolute_percentage_error: 107267832.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3315\n",
            "Epoch 36/50\n",
            "8/8 [==============================] - 3s 391ms/step - loss: 105758312.0000 - mean_absolute_percentage_error: 105758312.0000 - mean_absolute_error: 2.5002 - mean_squared_error: 11.4895 - val_loss: 103614120.0000 - val_mean_absolute_percentage_error: 103614120.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3357\n",
            "Epoch 37/50\n",
            "8/8 [==============================] - 3s 396ms/step - loss: 101861872.0000 - mean_absolute_percentage_error: 101861872.0000 - mean_absolute_error: 2.4995 - mean_squared_error: 11.5176 - val_loss: 100184448.0000 - val_mean_absolute_percentage_error: 100184448.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3398\n",
            "Epoch 38/50\n",
            "8/8 [==============================] - 3s 396ms/step - loss: 98628872.0000 - mean_absolute_percentage_error: 98628872.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 11.5537 - val_loss: 96449792.0000 - val_mean_absolute_percentage_error: 96449792.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3443\n",
            "Epoch 39/50\n",
            "8/8 [==============================] - 3s 391ms/step - loss: 94883832.0000 - mean_absolute_percentage_error: 94883832.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 11.5881 - val_loss: 92808120.0000 - val_mean_absolute_percentage_error: 92808120.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3488\n",
            "Epoch 40/50\n",
            "8/8 [==============================] - 3s 394ms/step - loss: 91169408.0000 - mean_absolute_percentage_error: 91169408.0000 - mean_absolute_error: 2.4998 - mean_squared_error: 11.6199 - val_loss: 89212688.0000 - val_mean_absolute_percentage_error: 89212688.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3534\n",
            "Epoch 41/50\n",
            "8/8 [==============================] - 3s 397ms/step - loss: 87528432.0000 - mean_absolute_percentage_error: 87528432.0000 - mean_absolute_error: 2.4998 - mean_squared_error: 11.6532 - val_loss: 85601400.0000 - val_mean_absolute_percentage_error: 85601400.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3581\n",
            "Epoch 42/50\n",
            "8/8 [==============================] - 3s 394ms/step - loss: 84108072.0000 - mean_absolute_percentage_error: 84108072.0000 - mean_absolute_error: 2.5004 - mean_squared_error: 11.6910 - val_loss: 81806832.0000 - val_mean_absolute_percentage_error: 81806832.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3632\n",
            "Epoch 43/50\n",
            "8/8 [==============================] - 3s 393ms/step - loss: 80087056.0000 - mean_absolute_percentage_error: 80087064.0000 - mean_absolute_error: 2.4995 - mean_squared_error: 11.7203 - val_loss: 78284288.0000 - val_mean_absolute_percentage_error: 78284288.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3679\n",
            "Epoch 44/50\n",
            "8/8 [==============================] - 3s 395ms/step - loss: 76551208.0000 - mean_absolute_percentage_error: 76551208.0000 - mean_absolute_error: 2.4997 - mean_squared_error: 11.7553 - val_loss: 74625512.0000 - val_mean_absolute_percentage_error: 74625512.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3730\n",
            "Epoch 45/50\n",
            "8/8 [==============================] - 3s 398ms/step - loss: 73261192.0000 - mean_absolute_percentage_error: 73261192.0000 - mean_absolute_error: 2.5008 - mean_squared_error: 11.7962 - val_loss: 70681040.0000 - val_mean_absolute_percentage_error: 70681040.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3786\n",
            "Epoch 46/50\n",
            "8/8 [==============================] - 3s 396ms/step - loss: 69025120.0000 - mean_absolute_percentage_error: 69025120.0000 - mean_absolute_error: 2.4998 - mean_squared_error: 11.8268 - val_loss: 67149176.0000 - val_mean_absolute_percentage_error: 67149176.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3837\n",
            "Epoch 47/50\n",
            "8/8 [==============================] - 4s 493ms/step - loss: 65656384.0000 - mean_absolute_percentage_error: 65656384.0000 - mean_absolute_error: 2.5004 - mean_squared_error: 11.8645 - val_loss: 63366828.0000 - val_mean_absolute_percentage_error: 63366828.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3893\n",
            "Epoch 48/50\n",
            "8/8 [==============================] - 3s 411ms/step - loss: 61855980.0000 - mean_absolute_percentage_error: 61855980.0000 - mean_absolute_error: 2.5002 - mean_squared_error: 11.8991 - val_loss: 59670484.0000 - val_mean_absolute_percentage_error: 59670484.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.3949\n",
            "Epoch 49/50\n",
            "8/8 [==============================] - 3s 390ms/step - loss: 58014196.0000 - mean_absolute_percentage_error: 58014196.0000 - mean_absolute_error: 2.4998 - mean_squared_error: 11.9316 - val_loss: 56087488.0000 - val_mean_absolute_percentage_error: 56087488.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4004\n",
            "Epoch 50/50\n",
            "8/8 [==============================] - 3s 396ms/step - loss: 54330348.0000 - mean_absolute_percentage_error: 54330348.0000 - mean_absolute_error: 2.4996 - mean_squared_error: 11.9643 - val_loss: 52496920.0000 - val_mean_absolute_percentage_error: 52496920.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4060\n",
            "2/2 [==============================] - 1s 105ms/step - loss: 52496928.0000 - mean_absolute_percentage_error: 52496928.0000 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4060\n",
            "\n",
            "[cirq.SWAP, cirq.ZZ, cirq.CZ, cirq.SWAP]\n",
            "Model: \"sequential_63\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " pqc_63 (PQC)                (None, 1)                 48        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 48\n",
            "Trainable params: 48\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/50\n",
            "8/8 [==============================] - 8s 965ms/step - loss: 18843.4961 - mean_absolute_percentage_error: 18843.4961 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4998 - val_loss: 18253.6602 - val_mean_absolute_percentage_error: 18253.6602 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 2/50\n",
            "8/8 [==============================] - 7s 944ms/step - loss: 17751.1816 - mean_absolute_percentage_error: 17751.1816 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4998 - val_loss: 17126.7891 - val_mean_absolute_percentage_error: 17126.7891 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 3/50\n",
            "8/8 [==============================] - 7s 940ms/step - loss: 16692.7734 - mean_absolute_percentage_error: 16692.7734 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4998 - val_loss: 16083.6934 - val_mean_absolute_percentage_error: 16083.6934 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 4/50\n",
            "8/8 [==============================] - 7s 937ms/step - loss: 15694.9629 - mean_absolute_percentage_error: 15694.9629 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4998 - val_loss: 15061.0996 - val_mean_absolute_percentage_error: 15061.0996 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 5/50\n",
            "8/8 [==============================] - 7s 938ms/step - loss: 14689.1357 - mean_absolute_percentage_error: 14689.1357 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4999 - val_loss: 14175.4277 - val_mean_absolute_percentage_error: 14175.4277 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 6/50\n",
            "8/8 [==============================] - 7s 939ms/step - loss: 13769.0410 - mean_absolute_percentage_error: 13769.0410 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4999 - val_loss: 13325.1299 - val_mean_absolute_percentage_error: 13325.1299 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 7/50\n",
            "8/8 [==============================] - 7s 935ms/step - loss: 12968.6924 - mean_absolute_percentage_error: 12968.6924 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4999 - val_loss: 12536.2998 - val_mean_absolute_percentage_error: 12536.2998 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 8/50\n",
            "8/8 [==============================] - 7s 945ms/step - loss: 12217.6035 - mean_absolute_percentage_error: 12217.6035 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4999 - val_loss: 11765.1650 - val_mean_absolute_percentage_error: 11765.1650 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 9/50\n",
            "8/8 [==============================] - 7s 931ms/step - loss: 11469.5176 - mean_absolute_percentage_error: 11469.5176 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4999 - val_loss: 11093.6816 - val_mean_absolute_percentage_error: 11093.6816 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 10/50\n",
            "8/8 [==============================] - 9s 1s/step - loss: 10814.0605 - mean_absolute_percentage_error: 10814.0605 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4999 - val_loss: 10467.8174 - val_mean_absolute_percentage_error: 10467.8174 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 11/50\n",
            "8/8 [==============================] - 7s 941ms/step - loss: 10213.3740 - mean_absolute_percentage_error: 10213.3740 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4999 - val_loss: 9863.3896 - val_mean_absolute_percentage_error: 9863.3896 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 12/50\n",
            "8/8 [==============================] - 7s 943ms/step - loss: 9603.0547 - mean_absolute_percentage_error: 9603.0547 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4999 - val_loss: 9292.5176 - val_mean_absolute_percentage_error: 9292.5176 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 13/50\n",
            "8/8 [==============================] - 7s 936ms/step - loss: 9070.2725 - mean_absolute_percentage_error: 9070.2725 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4999 - val_loss: 8754.1699 - val_mean_absolute_percentage_error: 8754.1699 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 14/50\n",
            "8/8 [==============================] - 7s 935ms/step - loss: 8564.9111 - mean_absolute_percentage_error: 8564.9111 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4999 - val_loss: 8264.2646 - val_mean_absolute_percentage_error: 8264.2646 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 15/50\n",
            "8/8 [==============================] - 7s 937ms/step - loss: 8084.6196 - mean_absolute_percentage_error: 8084.6196 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4999 - val_loss: 7783.6729 - val_mean_absolute_percentage_error: 7783.6729 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 16/50\n",
            "8/8 [==============================] - 7s 952ms/step - loss: 7617.7930 - mean_absolute_percentage_error: 7617.7930 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4999 - val_loss: 7387.0024 - val_mean_absolute_percentage_error: 7387.0024 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 17/50\n",
            "8/8 [==============================] - 7s 941ms/step - loss: 7216.0029 - mean_absolute_percentage_error: 7216.0029 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4999 - val_loss: 6961.3447 - val_mean_absolute_percentage_error: 6961.3447 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 18/50\n",
            "8/8 [==============================] - 7s 939ms/step - loss: 6818.9688 - mean_absolute_percentage_error: 6818.9688 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4999 - val_loss: 6584.1875 - val_mean_absolute_percentage_error: 6584.1875 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 19/50\n",
            "8/8 [==============================] - 7s 938ms/step - loss: 6428.4824 - mean_absolute_percentage_error: 6428.4824 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4999 - val_loss: 6226.5752 - val_mean_absolute_percentage_error: 6226.5752 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 20/50\n",
            "8/8 [==============================] - 7s 941ms/step - loss: 6090.8945 - mean_absolute_percentage_error: 6090.8945 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4999 - val_loss: 5906.2002 - val_mean_absolute_percentage_error: 5906.2002 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 21/50\n",
            "8/8 [==============================] - 9s 1s/step - loss: 5774.5635 - mean_absolute_percentage_error: 5774.5635 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4999 - val_loss: 5595.1382 - val_mean_absolute_percentage_error: 5595.1382 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 22/50\n",
            "8/8 [==============================] - 7s 936ms/step - loss: 5454.8999 - mean_absolute_percentage_error: 5454.8999 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4999 - val_loss: 5307.3887 - val_mean_absolute_percentage_error: 5307.3887 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 23/50\n",
            "8/8 [==============================] - 7s 935ms/step - loss: 5185.0337 - mean_absolute_percentage_error: 5185.0337 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4999 - val_loss: 5015.8848 - val_mean_absolute_percentage_error: 5015.8848 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 24/50\n",
            "8/8 [==============================] - 7s 948ms/step - loss: 4894.7812 - mean_absolute_percentage_error: 4894.7812 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 4753.2080 - val_mean_absolute_percentage_error: 4753.2080 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 25/50\n",
            "8/8 [==============================] - 7s 938ms/step - loss: 4648.1167 - mean_absolute_percentage_error: 4648.1167 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 4524.1465 - val_mean_absolute_percentage_error: 4524.1465 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 26/50\n",
            "8/8 [==============================] - 7s 941ms/step - loss: 4398.5894 - mean_absolute_percentage_error: 4398.5894 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 4265.1953 - val_mean_absolute_percentage_error: 4265.1953 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 27/50\n",
            "8/8 [==============================] - 7s 940ms/step - loss: 4187.0122 - mean_absolute_percentage_error: 4187.0122 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 4060.3040 - val_mean_absolute_percentage_error: 4060.3040 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 28/50\n",
            "8/8 [==============================] - 7s 943ms/step - loss: 3957.8977 - mean_absolute_percentage_error: 3957.8972 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 3833.0613 - val_mean_absolute_percentage_error: 3833.0613 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 29/50\n",
            "8/8 [==============================] - 7s 935ms/step - loss: 3753.1079 - mean_absolute_percentage_error: 3753.1079 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 3637.5710 - val_mean_absolute_percentage_error: 3637.5710 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 30/50\n",
            "8/8 [==============================] - 7s 938ms/step - loss: 3565.4863 - mean_absolute_percentage_error: 3565.4863 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 3476.4087 - val_mean_absolute_percentage_error: 3476.4087 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 31/50\n",
            "8/8 [==============================] - 7s 939ms/step - loss: 3390.4792 - mean_absolute_percentage_error: 3390.4792 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 3282.6929 - val_mean_absolute_percentage_error: 3282.6929 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 32/50\n",
            "8/8 [==============================] - 9s 1s/step - loss: 3210.0500 - mean_absolute_percentage_error: 3210.0500 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 3122.5205 - val_mean_absolute_percentage_error: 3122.5205 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 33/50\n",
            "8/8 [==============================] - 7s 940ms/step - loss: 3056.1033 - mean_absolute_percentage_error: 3056.1033 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 2956.7449 - val_mean_absolute_percentage_error: 2956.7449 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 34/50\n",
            "8/8 [==============================] - 7s 942ms/step - loss: 2893.7827 - mean_absolute_percentage_error: 2893.7827 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 2817.9490 - val_mean_absolute_percentage_error: 2817.9490 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 35/50\n",
            "8/8 [==============================] - 7s 943ms/step - loss: 2752.0808 - mean_absolute_percentage_error: 2752.0808 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 2654.9529 - val_mean_absolute_percentage_error: 2654.9529 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 36/50\n",
            "8/8 [==============================] - 7s 941ms/step - loss: 2604.5356 - mean_absolute_percentage_error: 2604.5356 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 2545.0713 - val_mean_absolute_percentage_error: 2545.0713 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 37/50\n",
            "8/8 [==============================] - 7s 944ms/step - loss: 2479.8350 - mean_absolute_percentage_error: 2479.8350 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 2422.1221 - val_mean_absolute_percentage_error: 2422.1221 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 38/50\n",
            "8/8 [==============================] - 7s 943ms/step - loss: 2356.7026 - mean_absolute_percentage_error: 2356.7026 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 2290.8496 - val_mean_absolute_percentage_error: 2290.8496 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 39/50\n",
            "8/8 [==============================] - 7s 942ms/step - loss: 2234.2324 - mean_absolute_percentage_error: 2234.2324 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 2169.7485 - val_mean_absolute_percentage_error: 2169.7485 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 40/50\n",
            "8/8 [==============================] - 7s 939ms/step - loss: 2113.7627 - mean_absolute_percentage_error: 2113.7627 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 2068.2051 - val_mean_absolute_percentage_error: 2068.2051 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 41/50\n",
            "8/8 [==============================] - 7s 947ms/step - loss: 2008.3181 - mean_absolute_percentage_error: 2008.3180 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 1934.9968 - val_mean_absolute_percentage_error: 1934.9968 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 42/50\n",
            "8/8 [==============================] - 7s 958ms/step - loss: 1907.0420 - mean_absolute_percentage_error: 1907.0420 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 1841.8500 - val_mean_absolute_percentage_error: 1841.8500 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 43/50\n",
            "8/8 [==============================] - 9s 1s/step - loss: 1802.0957 - mean_absolute_percentage_error: 1802.0957 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 1731.9977 - val_mean_absolute_percentage_error: 1731.9977 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 44/50\n",
            "8/8 [==============================] - 7s 954ms/step - loss: 1702.0652 - mean_absolute_percentage_error: 1702.0652 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 1648.1641 - val_mean_absolute_percentage_error: 1648.1641 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 45/50\n",
            "8/8 [==============================] - 7s 959ms/step - loss: 1614.8787 - mean_absolute_percentage_error: 1614.8787 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 1566.2076 - val_mean_absolute_percentage_error: 1566.2076 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 46/50\n",
            "8/8 [==============================] - 7s 957ms/step - loss: 1537.0994 - mean_absolute_percentage_error: 1537.0994 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 1487.9766 - val_mean_absolute_percentage_error: 1487.9766 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 47/50\n",
            "8/8 [==============================] - 7s 947ms/step - loss: 1446.1414 - mean_absolute_percentage_error: 1446.1414 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 1404.1284 - val_mean_absolute_percentage_error: 1404.1284 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 48/50\n",
            "8/8 [==============================] - 8s 975ms/step - loss: 1363.7332 - mean_absolute_percentage_error: 1363.7332 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 1330.5975 - val_mean_absolute_percentage_error: 1330.5975 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 49/50\n",
            "8/8 [==============================] - 7s 945ms/step - loss: 1292.9163 - mean_absolute_percentage_error: 1292.9163 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 1255.1459 - val_mean_absolute_percentage_error: 1255.1459 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 50/50\n",
            "8/8 [==============================] - 7s 945ms/step - loss: 1228.5596 - mean_absolute_percentage_error: 1228.5596 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 1170.3519 - val_mean_absolute_percentage_error: 1170.3519 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "2/2 [==============================] - 1s 222ms/step - loss: 1170.3519 - mean_absolute_percentage_error: 1170.3519 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000\n",
            "\n",
            "[cirq.YY, cirq.XX, cirq.CNOT]\n",
            "Model: \"sequential_64\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " pqc_64 (PQC)                (None, 1)                 12        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 12\n",
            "Trainable params: 12\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/50\n",
            "8/8 [==============================] - 4s 384ms/step - loss: 21501270.0000 - mean_absolute_percentage_error: 21501272.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.2867 - val_loss: 21241312.0000 - val_mean_absolute_percentage_error: 21241312.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4593\n",
            "Epoch 2/50\n",
            "8/8 [==============================] - 3s 353ms/step - loss: 21033958.0000 - mean_absolute_percentage_error: 21033958.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.2915 - val_loss: 20760180.0000 - val_mean_absolute_percentage_error: 20760180.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4602\n",
            "Epoch 3/50\n",
            "8/8 [==============================] - 3s 353ms/step - loss: 20575898.0000 - mean_absolute_percentage_error: 20575898.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.2964 - val_loss: 20288618.0000 - val_mean_absolute_percentage_error: 20288618.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4611\n",
            "Epoch 4/50\n",
            "8/8 [==============================] - 4s 536ms/step - loss: 20114848.0000 - mean_absolute_percentage_error: 20114848.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 12.3010 - val_loss: 19832106.0000 - val_mean_absolute_percentage_error: 19832106.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4619\n",
            "Epoch 5/50\n",
            "8/8 [==============================] - 3s 355ms/step - loss: 19654992.0000 - mean_absolute_percentage_error: 19654992.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3053 - val_loss: 19394364.0000 - val_mean_absolute_percentage_error: 19394364.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4627\n",
            "Epoch 6/50\n",
            "8/8 [==============================] - 3s 354ms/step - loss: 19207476.0000 - mean_absolute_percentage_error: 19207476.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3093 - val_loss: 18975802.0000 - val_mean_absolute_percentage_error: 18975802.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4635\n",
            "Epoch 7/50\n",
            "8/8 [==============================] - 3s 358ms/step - loss: 18805356.0000 - mean_absolute_percentage_error: 18805356.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3137 - val_loss: 18549804.0000 - val_mean_absolute_percentage_error: 18549804.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4643\n",
            "Epoch 8/50\n",
            "8/8 [==============================] - 3s 354ms/step - loss: 18370148.0000 - mean_absolute_percentage_error: 18370148.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3177 - val_loss: 18141120.0000 - val_mean_absolute_percentage_error: 18141120.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4650\n",
            "Epoch 9/50\n",
            "8/8 [==============================] - 3s 352ms/step - loss: 17959304.0000 - mean_absolute_percentage_error: 17959304.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3216 - val_loss: 17738694.0000 - val_mean_absolute_percentage_error: 17738694.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4658\n",
            "Epoch 10/50\n",
            "8/8 [==============================] - 3s 355ms/step - loss: 17560516.0000 - mean_absolute_percentage_error: 17560516.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3256 - val_loss: 17342012.0000 - val_mean_absolute_percentage_error: 17342012.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4665\n",
            "Epoch 11/50\n",
            "8/8 [==============================] - 3s 359ms/step - loss: 17160314.0000 - mean_absolute_percentage_error: 17160314.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3293 - val_loss: 16957020.0000 - val_mean_absolute_percentage_error: 16957020.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4672\n",
            "Epoch 12/50\n",
            "8/8 [==============================] - 3s 354ms/step - loss: 16813802.0000 - mean_absolute_percentage_error: 16813802.0000 - mean_absolute_error: 2.5001 - mean_squared_error: 12.3337 - val_loss: 16554699.0000 - val_mean_absolute_percentage_error: 16554699.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4680\n",
            "Epoch 13/50\n",
            "8/8 [==============================] - 3s 354ms/step - loss: 16383611.0000 - mean_absolute_percentage_error: 16383611.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3369 - val_loss: 16198323.0000 - val_mean_absolute_percentage_error: 16198323.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4687\n",
            "Epoch 14/50\n",
            "8/8 [==============================] - 3s 354ms/step - loss: 16022602.0000 - mean_absolute_percentage_error: 16022602.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3404 - val_loss: 15842467.0000 - val_mean_absolute_percentage_error: 15842467.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4693\n",
            "Epoch 15/50\n",
            "8/8 [==============================] - 3s 354ms/step - loss: 15658896.0000 - mean_absolute_percentage_error: 15658896.0000 - mean_absolute_error: 2.4999 - mean_squared_error: 12.3437 - val_loss: 15496658.0000 - val_mean_absolute_percentage_error: 15496658.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4700\n",
            "Epoch 16/50\n",
            "8/8 [==============================] - 3s 350ms/step - loss: 15343005.0000 - mean_absolute_percentage_error: 15343005.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3477 - val_loss: 15129603.0000 - val_mean_absolute_percentage_error: 15129603.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4707\n",
            "Epoch 17/50\n",
            "8/8 [==============================] - 3s 353ms/step - loss: 14982144.0000 - mean_absolute_percentage_error: 14982144.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3512 - val_loss: 14782256.0000 - val_mean_absolute_percentage_error: 14782256.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4713\n",
            "Epoch 18/50\n",
            "8/8 [==============================] - 3s 349ms/step - loss: 14647488.0000 - mean_absolute_percentage_error: 14647488.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3547 - val_loss: 14440395.0000 - val_mean_absolute_percentage_error: 14440395.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4720\n",
            "Epoch 19/50\n",
            "8/8 [==============================] - 3s 348ms/step - loss: 14291291.0000 - mean_absolute_percentage_error: 14291293.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3577 - val_loss: 14123989.0000 - val_mean_absolute_percentage_error: 14123989.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4726\n",
            "Epoch 20/50\n",
            "8/8 [==============================] - 3s 347ms/step - loss: 13977427.0000 - mean_absolute_percentage_error: 13977427.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3609 - val_loss: 13806581.0000 - val_mean_absolute_percentage_error: 13806581.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4731\n",
            "Epoch 21/50\n",
            "8/8 [==============================] - 3s 348ms/step - loss: 13676587.0000 - mean_absolute_percentage_error: 13676587.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3642 - val_loss: 13483928.0000 - val_mean_absolute_percentage_error: 13483928.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4738\n",
            "Epoch 22/50\n",
            "8/8 [==============================] - 3s 354ms/step - loss: 13340138.0000 - mean_absolute_percentage_error: 13340136.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3670 - val_loss: 13188318.0000 - val_mean_absolute_percentage_error: 13188318.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4743\n",
            "Epoch 23/50\n",
            "8/8 [==============================] - 3s 352ms/step - loss: 13037413.0000 - mean_absolute_percentage_error: 13037413.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3698 - val_loss: 12898247.0000 - val_mean_absolute_percentage_error: 12898247.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4749\n",
            "Epoch 24/50\n",
            "8/8 [==============================] - 3s 350ms/step - loss: 12765717.0000 - mean_absolute_percentage_error: 12765717.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3730 - val_loss: 12594604.0000 - val_mean_absolute_percentage_error: 12594604.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4754\n",
            "Epoch 25/50\n",
            "8/8 [==============================] - 3s 350ms/step - loss: 12461698.0000 - mean_absolute_percentage_error: 12461698.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3758 - val_loss: 12308510.0000 - val_mean_absolute_percentage_error: 12308510.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4760\n",
            "Epoch 26/50\n",
            "8/8 [==============================] - 3s 355ms/step - loss: 12173765.0000 - mean_absolute_percentage_error: 12173765.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3786 - val_loss: 12031425.0000 - val_mean_absolute_percentage_error: 12031425.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4765\n",
            "Epoch 27/50\n",
            "8/8 [==============================] - 3s 354ms/step - loss: 11894950.0000 - mean_absolute_percentage_error: 11894950.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3812 - val_loss: 11761632.0000 - val_mean_absolute_percentage_error: 11761632.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4770\n",
            "Epoch 28/50\n",
            "8/8 [==============================] - 3s 354ms/step - loss: 11632038.0000 - mean_absolute_percentage_error: 11632038.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3840 - val_loss: 11490641.0000 - val_mean_absolute_percentage_error: 11490641.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4775\n",
            "Epoch 29/50\n",
            "8/8 [==============================] - 3s 350ms/step - loss: 11371140.0000 - mean_absolute_percentage_error: 11371140.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3868 - val_loss: 11222362.0000 - val_mean_absolute_percentage_error: 11222362.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4781\n",
            "Epoch 30/50\n",
            "8/8 [==============================] - 3s 355ms/step - loss: 11090098.0000 - mean_absolute_percentage_error: 11090098.0000 - mean_absolute_error: 2.4999 - mean_squared_error: 12.3891 - val_loss: 10977375.0000 - val_mean_absolute_percentage_error: 10977375.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4785\n",
            "Epoch 31/50\n",
            "8/8 [==============================] - 3s 355ms/step - loss: 10859882.0000 - mean_absolute_percentage_error: 10859882.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3918 - val_loss: 10718824.0000 - val_mean_absolute_percentage_error: 10718824.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4790\n",
            "Epoch 32/50\n",
            "8/8 [==============================] - 3s 352ms/step - loss: 10616694.0000 - mean_absolute_percentage_error: 10616694.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3945 - val_loss: 10462510.0000 - val_mean_absolute_percentage_error: 10462510.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4795\n",
            "Epoch 33/50\n",
            "8/8 [==============================] - 4s 478ms/step - loss: 10359466.0000 - mean_absolute_percentage_error: 10359466.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3969 - val_loss: 10222890.0000 - val_mean_absolute_percentage_error: 10222890.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4800\n",
            "Epoch 34/50\n",
            "8/8 [==============================] - 3s 355ms/step - loss: 10124060.0000 - mean_absolute_percentage_error: 10124060.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.3993 - val_loss: 9987046.0000 - val_mean_absolute_percentage_error: 9987046.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4804\n",
            "Epoch 35/50\n",
            "8/8 [==============================] - 3s 352ms/step - loss: 9893558.0000 - mean_absolute_percentage_error: 9893558.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4016 - val_loss: 9755218.0000 - val_mean_absolute_percentage_error: 9755218.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4809\n",
            "Epoch 36/50\n",
            "8/8 [==============================] - 3s 352ms/step - loss: 9658814.0000 - mean_absolute_percentage_error: 9658814.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4038 - val_loss: 9534704.0000 - val_mean_absolute_percentage_error: 9534704.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4813\n",
            "Epoch 37/50\n",
            "8/8 [==============================] - 3s 357ms/step - loss: 9448762.0000 - mean_absolute_percentage_error: 9448762.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4061 - val_loss: 9310753.0000 - val_mean_absolute_percentage_error: 9310753.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4817\n",
            "Epoch 38/50\n",
            "8/8 [==============================] - 3s 351ms/step - loss: 9227593.0000 - mean_absolute_percentage_error: 9227593.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4083 - val_loss: 9095551.0000 - val_mean_absolute_percentage_error: 9095551.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4821\n",
            "Epoch 39/50\n",
            "8/8 [==============================] - 3s 355ms/step - loss: 9005453.0000 - mean_absolute_percentage_error: 9005453.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4102 - val_loss: 8892642.0000 - val_mean_absolute_percentage_error: 8892642.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4825\n",
            "Epoch 40/50\n",
            "8/8 [==============================] - 3s 351ms/step - loss: 8800434.0000 - mean_absolute_percentage_error: 8800434.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4122 - val_loss: 8692149.0000 - val_mean_absolute_percentage_error: 8692149.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4829\n",
            "Epoch 41/50\n",
            "8/8 [==============================] - 3s 353ms/step - loss: 8604090.0000 - mean_absolute_percentage_error: 8604090.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4143 - val_loss: 8491003.0000 - val_mean_absolute_percentage_error: 8491003.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4833\n",
            "Epoch 42/50\n",
            "8/8 [==============================] - 3s 352ms/step - loss: 8413881.0000 - mean_absolute_percentage_error: 8413881.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4164 - val_loss: 8288885.0000 - val_mean_absolute_percentage_error: 8288885.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4837\n",
            "Epoch 43/50\n",
            "8/8 [==============================] - 3s 349ms/step - loss: 8205048.0000 - mean_absolute_percentage_error: 8205048.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4182 - val_loss: 8102810.5000 - val_mean_absolute_percentage_error: 8102810.5000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4841\n",
            "Epoch 44/50\n",
            "8/8 [==============================] - 3s 355ms/step - loss: 8025281.0000 - mean_absolute_percentage_error: 8025281.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4202 - val_loss: 7913253.5000 - val_mean_absolute_percentage_error: 7913253.5000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4844\n",
            "Epoch 45/50\n",
            "8/8 [==============================] - 3s 357ms/step - loss: 7845557.0000 - mean_absolute_percentage_error: 7845557.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4221 - val_loss: 7724101.0000 - val_mean_absolute_percentage_error: 7724101.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4848\n",
            "Epoch 46/50\n",
            "8/8 [==============================] - 3s 349ms/step - loss: 7652689.0000 - mean_absolute_percentage_error: 7652689.0000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4238 - val_loss: 7547644.0000 - val_mean_absolute_percentage_error: 7547644.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4851\n",
            "Epoch 47/50\n",
            "8/8 [==============================] - 3s 352ms/step - loss: 7482237.5000 - mean_absolute_percentage_error: 7482237.5000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4256 - val_loss: 7370152.0000 - val_mean_absolute_percentage_error: 7370152.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4855\n",
            "Epoch 48/50\n",
            "8/8 [==============================] - 3s 358ms/step - loss: 7307593.5000 - mean_absolute_percentage_error: 7307593.5000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4274 - val_loss: 7197486.5000 - val_mean_absolute_percentage_error: 7197486.5000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4858\n",
            "Epoch 49/50\n",
            "8/8 [==============================] - 3s 357ms/step - loss: 7129510.5000 - mean_absolute_percentage_error: 7129510.5000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4290 - val_loss: 7035320.0000 - val_mean_absolute_percentage_error: 7035320.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4861\n",
            "Epoch 50/50\n",
            "8/8 [==============================] - 3s 352ms/step - loss: 6969137.5000 - mean_absolute_percentage_error: 6969137.5000 - mean_absolute_error: 2.5000 - mean_squared_error: 12.4306 - val_loss: 6872899.0000 - val_mean_absolute_percentage_error: 6872899.0000 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.4864\n",
            "2/2 [==============================] - 1s 96ms/step - loss: 6872901.0000 - mean_absolute_percentage_error: 6872901.0000 - mean_absolute_error: 0.5000 - mean_squared_error: 0.4864\n",
            "\n",
            "[cirq.YY, cirq.XX, cirq.CNOT]\n",
            "Model: \"sequential_65\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " pqc_65 (PQC)                (None, 1)                 48        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 48\n",
            "Trainable params: 48\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/50\n",
            "8/8 [==============================] - 7s 782ms/step - loss: 1635.9381 - mean_absolute_percentage_error: 1635.9381 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 1510.6221 - val_mean_absolute_percentage_error: 1510.6221 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 2/50\n",
            "8/8 [==============================] - 6s 754ms/step - loss: 1431.2356 - mean_absolute_percentage_error: 1431.2356 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 1365.4509 - val_mean_absolute_percentage_error: 1365.4509 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 3/50\n",
            "8/8 [==============================] - 6s 760ms/step - loss: 1270.9883 - mean_absolute_percentage_error: 1270.9883 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 1147.1726 - val_mean_absolute_percentage_error: 1147.1726 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 4/50\n",
            "8/8 [==============================] - 6s 752ms/step - loss: 1114.5237 - mean_absolute_percentage_error: 1114.5237 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 1021.0231 - val_mean_absolute_percentage_error: 1021.0231 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 5/50\n",
            "8/8 [==============================] - 6s 750ms/step - loss: 959.9418 - mean_absolute_percentage_error: 959.9418 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 909.6415 - val_mean_absolute_percentage_error: 909.6415 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 6/50\n",
            "8/8 [==============================] - 7s 944ms/step - loss: 864.8551 - mean_absolute_percentage_error: 864.8551 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 817.4374 - val_mean_absolute_percentage_error: 817.4374 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 7/50\n",
            "8/8 [==============================] - 6s 752ms/step - loss: 761.5217 - mean_absolute_percentage_error: 761.5217 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 742.2774 - val_mean_absolute_percentage_error: 742.2774 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 8/50\n",
            "8/8 [==============================] - 6s 753ms/step - loss: 673.2612 - mean_absolute_percentage_error: 673.2612 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 645.3007 - val_mean_absolute_percentage_error: 645.3007 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 9/50\n",
            "8/8 [==============================] - 6s 743ms/step - loss: 603.1595 - mean_absolute_percentage_error: 603.1595 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 570.0488 - val_mean_absolute_percentage_error: 570.0488 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 10/50\n",
            "8/8 [==============================] - 6s 755ms/step - loss: 535.5090 - mean_absolute_percentage_error: 535.5090 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 503.7272 - val_mean_absolute_percentage_error: 503.7272 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 11/50\n",
            "8/8 [==============================] - 6s 750ms/step - loss: 479.0814 - mean_absolute_percentage_error: 479.0814 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 435.8354 - val_mean_absolute_percentage_error: 435.8354 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 12/50\n",
            "8/8 [==============================] - 6s 749ms/step - loss: 422.3661 - mean_absolute_percentage_error: 422.3661 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 400.9026 - val_mean_absolute_percentage_error: 400.9026 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 13/50\n",
            "8/8 [==============================] - 6s 755ms/step - loss: 381.1446 - mean_absolute_percentage_error: 381.1446 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 344.4221 - val_mean_absolute_percentage_error: 344.4221 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 14/50\n",
            "8/8 [==============================] - 6s 749ms/step - loss: 336.8714 - mean_absolute_percentage_error: 336.8714 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 332.3267 - val_mean_absolute_percentage_error: 332.3267 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 15/50\n",
            "8/8 [==============================] - 6s 755ms/step - loss: 314.5138 - mean_absolute_percentage_error: 314.5138 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 289.5129 - val_mean_absolute_percentage_error: 289.5129 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 16/50\n",
            "8/8 [==============================] - 6s 751ms/step - loss: 274.1088 - mean_absolute_percentage_error: 274.1088 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 244.2570 - val_mean_absolute_percentage_error: 244.2570 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 17/50\n",
            "8/8 [==============================] - 6s 755ms/step - loss: 236.8575 - mean_absolute_percentage_error: 236.8575 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 212.1585 - val_mean_absolute_percentage_error: 212.1585 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 18/50\n",
            "8/8 [==============================] - 6s 755ms/step - loss: 195.9995 - mean_absolute_percentage_error: 195.9995 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 165.5990 - val_mean_absolute_percentage_error: 165.5990 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 19/50\n",
            "8/8 [==============================] - 7s 951ms/step - loss: 175.3293 - mean_absolute_percentage_error: 175.3293 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 149.1241 - val_mean_absolute_percentage_error: 149.1241 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 20/50\n",
            "8/8 [==============================] - 6s 758ms/step - loss: 150.1579 - mean_absolute_percentage_error: 150.1579 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 154.2130 - val_mean_absolute_percentage_error: 154.2130 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 21/50\n",
            "8/8 [==============================] - 6s 750ms/step - loss: 126.6820 - mean_absolute_percentage_error: 126.6820 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 104.9112 - val_mean_absolute_percentage_error: 104.9112 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 22/50\n",
            "8/8 [==============================] - 6s 756ms/step - loss: 101.2801 - mean_absolute_percentage_error: 101.2801 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 107.9138 - val_mean_absolute_percentage_error: 107.9138 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 23/50\n",
            "8/8 [==============================] - 6s 753ms/step - loss: 92.3746 - mean_absolute_percentage_error: 92.3746 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 55.9723 - val_mean_absolute_percentage_error: 55.9723 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 24/50\n",
            "8/8 [==============================] - 6s 755ms/step - loss: 66.2006 - mean_absolute_percentage_error: 66.2006 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 87.7024 - val_mean_absolute_percentage_error: 87.7024 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 25/50\n",
            "8/8 [==============================] - 6s 754ms/step - loss: 67.6575 - mean_absolute_percentage_error: 67.6575 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 52.6894 - val_mean_absolute_percentage_error: 52.6894 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 26/50\n",
            "8/8 [==============================] - 6s 747ms/step - loss: 59.8160 - mean_absolute_percentage_error: 59.8160 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 59.8543 - val_mean_absolute_percentage_error: 59.8543 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 27/50\n",
            "8/8 [==============================] - 6s 750ms/step - loss: 60.3417 - mean_absolute_percentage_error: 60.3417 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 73.9791 - val_mean_absolute_percentage_error: 73.9791 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 28/50\n",
            "8/8 [==============================] - 6s 748ms/step - loss: 59.2843 - mean_absolute_percentage_error: 59.2843 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 53.8620 - val_mean_absolute_percentage_error: 53.8620 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 29/50\n",
            "8/8 [==============================] - 6s 753ms/step - loss: 60.9393 - mean_absolute_percentage_error: 60.9393 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 60.0900 - val_mean_absolute_percentage_error: 60.0900 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 30/50\n",
            "8/8 [==============================] - 6s 747ms/step - loss: 66.6834 - mean_absolute_percentage_error: 66.6834 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 78.9137 - val_mean_absolute_percentage_error: 78.9137 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "Epoch 31/50\n",
            "8/8 [==============================] - 6s 746ms/step - loss: 73.5358 - mean_absolute_percentage_error: 73.5358 - mean_absolute_error: 2.5000 - mean_squared_error: 12.5000 - val_loss: 51.2462 - val_mean_absolute_percentage_error: 51.2462 - val_mean_absolute_error: 0.5000 - val_mean_squared_error: 0.5000\n",
            "2/2 [==============================] - 1s 172ms/step - loss: 51.2462 - mean_absolute_percentage_error: 51.2462 - mean_absolute_error: 0.5000 - mean_squared_error: 0.5000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Circuit #1 - design and results"
      ],
      "metadata": {
        "id": "qqdz8mIe34KG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SVGCircuit(experiment_reveal(experiment_log, 1))"
      ],
      "metadata": {
        "id": "Tx7EhD4J2cEg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "outputId": "540c64a5-c897-47d9-d0c6-053d5fc5d037"
      },
      "execution_count": null,
      "outputs": [
        {
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            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Circuit #2 - design and results"
      ],
      "metadata": {
        "id": "_B8KZu4-4IYN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SVGCircuit(experiment_reveal(experiment_log, 2))"
      ],
      "metadata": {
        "id": "m5sbC9cG4IYS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "30c0984a-a8d2-440b-876b-859cf714bdb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<cirq.contrib.svg.svg.SVGCircuit at 0x7f182c6166d0>"
            ],
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x=\"39.810625\" y=\"775.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">(3, 2): </text><rect x=\"10.0\" y=\"805.0\" width=\"59.62125\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"0\" /><text x=\"39.810625\" y=\"825.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">(3, 3): </text><rect x=\"89.62125\" y=\"5.0\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"109.62125\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"18px\" font-family=\"Arial\">X</text><rect x=\"149.62125\" y=\"5.0\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"169.62125\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"18px\" font-family=\"Arial\">H</text><rect x=\"209.62125\" y=\"55.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"250.94919921875\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-0)</text><rect x=\"209.62125\" y=\"5.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"250.94919921875\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"312.27714843750005\" y=\"105.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"353.60509765625005\" y=\"125.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-1)</text><rect x=\"312.27714843750005\" y=\"5.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"353.60509765625005\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"414.93304687500006\" y=\"155.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"456.26099609375007\" y=\"175.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-2)</text><rect x=\"414.93304687500006\" y=\"5.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"456.26099609375007\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"517.5889453125001\" y=\"205.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"558.9168945312501\" y=\"225.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-3)</text><rect x=\"517.5889453125001\" y=\"5.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"558.9168945312501\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"620.24484375\" y=\"255.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"661.57279296875\" y=\"275.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-4)</text><rect x=\"620.24484375\" y=\"5.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"661.57279296875\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"722.9007421875001\" y=\"305.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"764.2286914062502\" y=\"325.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-5)</text><rect x=\"722.9007421875001\" y=\"5.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"764.2286914062502\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"825.556640625\" y=\"355.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"866.8845898437501\" y=\"375.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-6)</text><rect x=\"825.556640625\" y=\"5.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"866.8845898437501\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"928.2125390625001\" y=\"405.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"969.5404882812502\" y=\"425.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-7)</text><rect x=\"928.2125390625001\" y=\"5.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"969.5404882812502\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"1030.8684375\" y=\"455.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1072.19638671875\" y=\"475.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-8)</text><rect x=\"1030.8684375\" y=\"5.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1072.19638671875\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"1133.5243359375002\" y=\"505.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1174.8522851562502\" y=\"525.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-9)</text><rect x=\"1133.5243359375002\" y=\"5.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1174.8522851562502\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"1236.180234375\" y=\"555.0\" width=\"91.56312500000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1281.9617968750001\" y=\"575.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-10)</text><rect x=\"1236.180234375\" y=\"5.0\" width=\"91.56312500000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1281.9617968750001\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"1347.7433593750002\" y=\"605.0\" width=\"91.56312500000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1393.5249218750002\" y=\"625.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-11)</text><rect x=\"1347.7433593750002\" y=\"5.0\" width=\"91.56312500000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1393.5249218750002\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"1459.3064843750003\" y=\"655.0\" width=\"91.56312500000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1505.0880468750004\" y=\"675.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-12)</text><rect x=\"1459.3064843750003\" y=\"5.0\" width=\"91.56312500000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1505.0880468750004\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"1570.8696093750004\" y=\"705.0\" width=\"91.56312500000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1616.6511718750005\" y=\"725.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-13)</text><rect x=\"1570.8696093750004\" y=\"5.0\" width=\"91.56312500000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1616.6511718750005\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"1682.4327343750006\" y=\"755.0\" width=\"91.56312500000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1728.2142968750006\" y=\"775.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-14)</text><rect x=\"1682.4327343750006\" y=\"5.0\" width=\"91.56312500000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1728.2142968750006\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"1793.9958593750007\" y=\"805.0\" width=\"91.56312500000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1839.7774218750008\" y=\"825.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-15)</text><rect x=\"1793.9958593750007\" y=\"5.0\" width=\"91.56312500000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1839.7774218750008\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"1905.5589843750008\" y=\"5.0\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1925.5589843750008\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"18px\" font-family=\"Arial\">H</text></svg>"
          },
          "metadata": {},
          "execution_count": 147
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x540 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Circuit #3 - design and results"
      ],
      "metadata": {
        "id": "fYv35ZI44aMk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SVGCircuit(experiment_reveal(experiment_log, 3))"
      ],
      "metadata": {
        "id": "RbV0WgAU4aMp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "outputId": "46a4d462-8bbd-431e-c195-7644b0d5c398"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<cirq.contrib.svg.svg.SVGCircuit at 0x7f181e8ce410>"
            ],
            "image/svg+xml": "<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"897.9279687500001\" height=\"250.0\"><line x1=\"39.810625\" x2=\"867.9279687500001\" y1=\"25.0\" y2=\"25.0\" stroke=\"#1967d2\" stroke-width=\"1\" /><line x1=\"39.810625\" x2=\"867.9279687500001\" y1=\"75.0\" y2=\"75.0\" stroke=\"#1967d2\" stroke-width=\"1\" /><line x1=\"39.810625\" x2=\"867.9279687500001\" y1=\"125.0\" y2=\"125.0\" stroke=\"#1967d2\" stroke-width=\"1\" /><line x1=\"39.810625\" x2=\"867.9279687500001\" y1=\"175.0\" y2=\"175.0\" stroke=\"#1967d2\" stroke-width=\"1\" /><line x1=\"39.810625\" x2=\"867.9279687500001\" y1=\"225.0\" y2=\"225.0\" stroke=\"#1967d2\" stroke-width=\"1\" /><line x1=\"274.40958984375004\" x2=\"274.40958984375004\" y1=\"25.0\" y2=\"75.0\" stroke=\"black\" stroke-width=\"3\" /><line x1=\"423.98626953125006\" x2=\"423.98626953125006\" y1=\"25.0\" y2=\"125.0\" stroke=\"black\" stroke-width=\"3\" /><line x1=\"573.5629492187501\" x2=\"573.5629492187501\" y1=\"25.0\" 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width=\"129.57667968750002\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"274.40958984375004\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">iSwap^(ISWAP-0)</text><rect x=\"209.62125000000003\" y=\"5.0\" width=\"129.57667968750002\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"274.40958984375004\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">iSwap</text><rect x=\"359.19792968750005\" y=\"105.0\" width=\"129.57667968750002\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"423.98626953125006\" y=\"125.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">iSwap^(ISWAP-1)</text><rect x=\"359.19792968750005\" y=\"5.0\" width=\"129.57667968750002\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"423.98626953125006\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">iSwap</text><rect x=\"508.77460937500007\" y=\"155.0\" width=\"129.57667968750002\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"573.5629492187501\" y=\"175.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">iSwap^(ISWAP-2)</text><rect x=\"508.77460937500007\" y=\"5.0\" width=\"129.57667968750002\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"573.5629492187501\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">iSwap</text><rect x=\"658.3512890625001\" y=\"205.0\" width=\"129.57667968750002\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"723.1396289062501\" y=\"225.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">iSwap^(ISWAP-3)</text><rect x=\"658.3512890625001\" y=\"5.0\" width=\"129.57667968750002\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"723.1396289062501\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">iSwap</text><rect x=\"807.9279687500001\" y=\"5.0\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"827.9279687500001\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"18px\" font-family=\"Arial\">H</text></svg>"
          },
          "metadata": {},
          "execution_count": 148
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x540 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Circuit #4 - design and results"
      ],
      "metadata": {
        "id": "svRcWAjn45ZQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SVGCircuit(experiment_reveal(experiment_log, 4))"
      ],
      "metadata": {
        "id": "7xoCWr3a45ZU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2343b92f-01d0-4897-e446-6b47406350ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<cirq.contrib.svg.svg.SVGCircuit at 0x7f181e83b6d0>"
            ],
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          },
          "metadata": {},
          "execution_count": 149
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x540 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Circuit #5 - design and results"
      ],
      "metadata": {
        "id": "sDBay62S5Km9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SVGCircuit(experiment_reveal(experiment_log, 5))"
      ],
      "metadata": {
        "id": "D0nACPi55KnC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "outputId": "74fc1fef-4f22-43c0-9421-b5bc427c0b9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<cirq.contrib.svg.svg.SVGCircuit at 0x7f181e66ef10>"
            ],
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          },
          "metadata": {},
          "execution_count": 150
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x540 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Circuit #6 - design and results"
      ],
      "metadata": {
        "id": "MtyRVZkD5X5O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SVGCircuit(experiment_reveal(experiment_log, 6))"
      ],
      "metadata": {
        "id": "OJ7521sc5X5T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "36e70c3f-15c1-417f-bcb5-e2ac95f26c78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<cirq.contrib.svg.svg.SVGCircuit at 0x7f181e54efd0>"
            ],
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          },
          "metadata": {},
          "execution_count": 151
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x540 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Circuit #7 - design and results"
      ],
      "metadata": {
        "id": "zvP9Tzd65kec"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SVGCircuit(experiment_reveal(experiment_log, 7))"
      ],
      "metadata": {
        "id": "Cf-XNQ-G5keh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 755
        },
        "outputId": "3227fad2-558f-47d5-a954-99ede1a106fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<cirq.contrib.svg.svg.SVGCircuit at 0x7f181e4b8750>"
            ],
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font-family=\"Arial\">ZZ^(ZZ-3)</text><rect x=\"977.8545703125001\" y=\"5.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1019.3039257812501\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ</text><rect x=\"1080.75328125\" y=\"55.0\" width=\"77.60851562500001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1119.5575390625002\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">@^(CZ-0)</text><circle cx=\"1119.5575390625002\" cy=\"25.0\" r=\"10.0\" /><rect x=\"1178.3617968750002\" y=\"105.0\" width=\"77.60851562500001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1217.1660546875003\" y=\"125.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">@^(CZ-1)</text><circle cx=\"1217.1660546875003\" cy=\"25.0\" r=\"10.0\" /><rect x=\"1275.9703125000003\" y=\"155.0\" width=\"77.60851562500001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1314.7745703125004\" y=\"175.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">@^(CZ-2)</text><circle cx=\"1314.7745703125004\" cy=\"25.0\" r=\"10.0\" /><rect x=\"1373.5788281250004\" y=\"205.0\" width=\"77.60851562500001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1412.3830859375005\" y=\"225.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">@^(CZ-3)</text><circle cx=\"1412.3830859375005\" cy=\"25.0\" r=\"10.0\" /><rect x=\"1471.1873437500005\" y=\"55.0\" width=\"94.884296875\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1518.6294921875005\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-0)</text><text x=\"1518.6294921875005\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"1586.0716406250006\" y=\"105.0\" width=\"94.884296875\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1633.5137890625006\" y=\"125.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-1)</text><text x=\"1633.5137890625006\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"1700.9559375000006\" y=\"155.0\" width=\"94.884296875\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1748.3980859375006\" y=\"175.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-2)</text><text x=\"1748.3980859375006\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"1815.8402343750006\" y=\"205.0\" width=\"94.884296875\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1863.2823828125006\" y=\"225.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-3)</text><text x=\"1863.2823828125006\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"1930.7245312500006\" y=\"5.0\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1950.7245312500006\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"18px\" font-family=\"Arial\">H</text></svg>"
          },
          "metadata": {},
          "execution_count": 152
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x540 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Circuit #8 - design and results"
      ],
      "metadata": {
        "id": "b7iWeAiR53pm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SVGCircuit(experiment_reveal(experiment_log, 8))"
      ],
      "metadata": {
        "id": "qtBk5DPx53ps",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9d5fe905-a852-4c74-818f-a284d3e06154"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<cirq.contrib.svg.svg.SVGCircuit at 0x7f181e38f790>"
            ],
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font-size=\"14px\" font-family=\"Arial\">×^(SWAP-10)</text><text x=\"1410.35998046875\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"1482.2557421875001\" y=\"605.0\" width=\"103.7915234375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1534.1515039062501\" y=\"625.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-11)</text><text x=\"1534.1515039062501\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"1606.0472656250001\" y=\"655.0\" width=\"103.7915234375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1657.9430273437501\" y=\"675.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-12)</text><text x=\"1657.9430273437501\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"1729.8387890625002\" y=\"705.0\" width=\"103.7915234375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1781.7345507812502\" y=\"725.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-13)</text><text x=\"1781.7345507812502\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"1853.6303125000002\" y=\"755.0\" width=\"103.7915234375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1905.5260742187502\" y=\"775.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-14)</text><text x=\"1905.5260742187502\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"1977.4218359375002\" y=\"805.0\" width=\"103.7915234375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2029.3175976562502\" y=\"825.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-15)</text><text x=\"2029.3175976562502\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"2101.213359375\" y=\"55.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2142.66271484375\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ^(ZZ-0)</text><rect x=\"2101.213359375\" y=\"5.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2142.66271484375\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ</text><rect x=\"2204.1120703125\" y=\"105.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2245.56142578125\" y=\"125.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ^(ZZ-1)</text><rect x=\"2204.1120703125\" y=\"5.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2245.56142578125\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ</text><rect x=\"2307.0107812499996\" y=\"155.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2348.4601367187497\" y=\"175.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ^(ZZ-2)</text><rect x=\"2307.0107812499996\" y=\"5.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2348.4601367187497\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ</text><rect x=\"2409.9094921874994\" y=\"205.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2451.3588476562495\" y=\"225.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ^(ZZ-3)</text><rect x=\"2409.9094921874994\" y=\"5.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2451.3588476562495\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ</text><rect x=\"2512.808203124999\" y=\"255.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2554.2575585937493\" y=\"275.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ^(ZZ-4)</text><rect x=\"2512.808203124999\" y=\"5.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2554.2575585937493\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ</text><rect x=\"2615.706914062499\" y=\"305.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2657.156269531249\" y=\"325.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ^(ZZ-5)</text><rect x=\"2615.706914062499\" y=\"5.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2657.156269531249\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ</text><rect x=\"2718.605624999999\" y=\"355.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2760.054980468749\" y=\"375.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ^(ZZ-6)</text><rect x=\"2718.605624999999\" y=\"5.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2760.054980468749\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ</text><rect x=\"2821.5043359374986\" y=\"405.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2862.9536914062487\" y=\"425.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ^(ZZ-7)</text><rect x=\"2821.5043359374986\" y=\"5.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2862.9536914062487\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ</text><rect x=\"2924.4030468749984\" y=\"455.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2965.8524023437485\" y=\"475.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ^(ZZ-8)</text><rect x=\"2924.4030468749984\" y=\"5.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2965.8524023437485\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ</text><rect x=\"3027.301757812498\" y=\"505.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"3068.7511132812483\" y=\"525.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ^(ZZ-9)</text><rect x=\"3027.301757812498\" y=\"5.0\" width=\"82.89871093750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"3068.7511132812483\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ</text><rect x=\"3130.200468749998\" y=\"555.0\" width=\"91.80593750000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"3176.103437499998\" y=\"575.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ^(ZZ-10)</text><rect x=\"3130.200468749998\" y=\"5.0\" width=\"91.80593750000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"3176.103437499998\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ</text><rect x=\"3242.006406249998\" y=\"605.0\" width=\"91.80593750000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"3287.909374999998\" y=\"625.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ^(ZZ-11)</text><rect x=\"3242.006406249998\" y=\"5.0\" width=\"91.80593750000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"3287.909374999998\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ</text><rect x=\"3353.8123437499976\" y=\"655.0\" width=\"91.80593750000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"3399.7153124999977\" y=\"675.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ^(ZZ-12)</text><rect x=\"3353.8123437499976\" y=\"5.0\" width=\"91.80593750000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"3399.7153124999977\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ</text><rect x=\"3465.6182812499974\" y=\"705.0\" width=\"91.80593750000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"3511.5212499999975\" y=\"725.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ^(ZZ-13)</text><rect x=\"3465.6182812499974\" y=\"5.0\" width=\"91.80593750000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"3511.5212499999975\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ</text><rect x=\"3577.424218749997\" y=\"755.0\" width=\"91.80593750000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"3623.3271874999973\" y=\"775.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ^(ZZ-14)</text><rect x=\"3577.424218749997\" y=\"5.0\" width=\"91.80593750000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"3623.3271874999973\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ</text><rect x=\"3689.230156249997\" y=\"805.0\" width=\"91.80593750000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"3735.133124999997\" y=\"825.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ^(ZZ-15)</text><rect x=\"3689.230156249997\" y=\"5.0\" width=\"91.80593750000001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"3735.133124999997\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">ZZ</text><rect x=\"3801.036093749997\" y=\"55.0\" width=\"77.60851562500001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"3839.840351562497\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">@^(CZ-0)</text><circle cx=\"3839.840351562497\" cy=\"25.0\" r=\"10.0\" /><rect x=\"3898.644609374997\" y=\"105.0\" width=\"77.60851562500001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"3937.448867187497\" y=\"125.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">@^(CZ-1)</text><circle cx=\"3937.448867187497\" cy=\"25.0\" r=\"10.0\" /><rect x=\"3996.253124999997\" y=\"155.0\" width=\"77.60851562500001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"4035.057382812497\" y=\"175.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">@^(CZ-2)</text><circle cx=\"4035.057382812497\" cy=\"25.0\" r=\"10.0\" /><rect x=\"4093.861640624997\" y=\"205.0\" width=\"77.60851562500001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"4132.665898437497\" y=\"225.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">@^(CZ-3)</text><circle cx=\"4132.665898437497\" cy=\"25.0\" r=\"10.0\" /><rect x=\"4191.470156249997\" y=\"255.0\" width=\"77.60851562500001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"4230.274414062496\" y=\"275.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">@^(CZ-4)</text><circle cx=\"4230.274414062496\" cy=\"25.0\" r=\"10.0\" /><rect x=\"4289.078671874997\" y=\"305.0\" width=\"77.60851562500001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"4327.8829296874965\" y=\"325.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">@^(CZ-5)</text><circle cx=\"4327.8829296874965\" cy=\"25.0\" r=\"10.0\" /><rect x=\"4386.687187499997\" y=\"355.0\" width=\"77.60851562500001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"4425.491445312497\" y=\"375.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">@^(CZ-6)</text><circle cx=\"4425.491445312497\" cy=\"25.0\" r=\"10.0\" /><rect x=\"4484.295703124997\" y=\"405.0\" width=\"77.60851562500001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"4523.099960937497\" y=\"425.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">@^(CZ-7)</text><circle cx=\"4523.099960937497\" cy=\"25.0\" r=\"10.0\" /><rect x=\"4581.904218749997\" y=\"455.0\" width=\"77.60851562500001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"4620.708476562497\" y=\"475.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">@^(CZ-8)</text><circle cx=\"4620.708476562497\" cy=\"25.0\" r=\"10.0\" /><rect x=\"4679.512734374997\" y=\"505.0\" width=\"77.60851562500001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"4718.316992187497\" y=\"525.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">@^(CZ-9)</text><circle cx=\"4718.316992187497\" cy=\"25.0\" r=\"10.0\" /><rect x=\"4777.121249999997\" y=\"555.0\" width=\"86.51574218750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"4820.379121093747\" y=\"575.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">@^(CZ-10)</text><circle cx=\"4820.379121093747\" cy=\"25.0\" r=\"10.0\" /><rect x=\"4883.6369921874975\" y=\"605.0\" width=\"86.51574218750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"4926.894863281247\" y=\"625.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">@^(CZ-11)</text><circle cx=\"4926.894863281247\" cy=\"25.0\" r=\"10.0\" /><rect x=\"4990.152734374998\" y=\"655.0\" width=\"86.51574218750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"5033.410605468747\" y=\"675.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">@^(CZ-12)</text><circle cx=\"5033.410605468747\" cy=\"25.0\" r=\"10.0\" /><rect x=\"5096.668476562498\" y=\"705.0\" width=\"86.51574218750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"5139.926347656247\" y=\"725.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">@^(CZ-13)</text><circle cx=\"5139.926347656247\" cy=\"25.0\" r=\"10.0\" /><rect x=\"5203.184218749998\" y=\"755.0\" width=\"86.51574218750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"5246.442089843747\" y=\"775.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">@^(CZ-14)</text><circle cx=\"5246.442089843747\" cy=\"25.0\" r=\"10.0\" /><rect x=\"5309.699960937498\" y=\"805.0\" width=\"86.51574218750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"5352.957832031248\" y=\"825.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">@^(CZ-15)</text><circle cx=\"5352.957832031248\" cy=\"25.0\" r=\"10.0\" /><rect x=\"5416.215703124998\" y=\"55.0\" width=\"94.884296875\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"5463.657851562498\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-0)</text><text x=\"5463.657851562498\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"5531.099999999998\" y=\"105.0\" width=\"94.884296875\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"5578.542148437497\" y=\"125.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-1)</text><text x=\"5578.542148437497\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"5645.984296874997\" y=\"155.0\" width=\"94.884296875\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"5693.426445312497\" y=\"175.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-2)</text><text x=\"5693.426445312497\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"5760.868593749997\" y=\"205.0\" width=\"94.884296875\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"5808.3107421874965\" y=\"225.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-3)</text><text x=\"5808.3107421874965\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"5875.752890624996\" y=\"255.0\" width=\"94.884296875\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"5923.195039062496\" y=\"275.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-4)</text><text x=\"5923.195039062496\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"5990.637187499996\" y=\"305.0\" width=\"94.884296875\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"6038.079335937496\" y=\"325.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-5)</text><text x=\"6038.079335937496\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"6105.521484374995\" y=\"355.0\" width=\"94.884296875\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"6152.963632812495\" y=\"375.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-6)</text><text x=\"6152.963632812495\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"6220.405781249995\" y=\"405.0\" width=\"94.884296875\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"6267.847929687495\" y=\"425.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-7)</text><text x=\"6267.847929687495\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"6335.290078124995\" y=\"455.0\" width=\"94.884296875\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"6382.732226562494\" y=\"475.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-8)</text><text x=\"6382.732226562494\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"6450.174374999994\" y=\"505.0\" width=\"94.884296875\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"6497.616523437494\" y=\"525.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-9)</text><text x=\"6497.616523437494\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"6565.058671874994\" y=\"555.0\" width=\"103.7915234375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"6616.9544335937435\" y=\"575.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-10)</text><text x=\"6616.9544335937435\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"6688.850195312493\" y=\"605.0\" width=\"103.7915234375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"6740.745957031243\" y=\"625.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-11)</text><text x=\"6740.745957031243\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"6812.641718749993\" y=\"655.0\" width=\"103.7915234375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"6864.537480468743\" y=\"675.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-12)</text><text x=\"6864.537480468743\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"6936.433242187492\" y=\"705.0\" width=\"103.7915234375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"6988.329003906242\" y=\"725.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-13)</text><text x=\"6988.329003906242\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"7060.224765624992\" y=\"755.0\" width=\"103.7915234375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"7112.120527343742\" y=\"775.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-14)</text><text x=\"7112.120527343742\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"7184.0162890624915\" y=\"805.0\" width=\"103.7915234375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"7235.912050781241\" y=\"825.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">×^(SWAP-15)</text><text x=\"7235.912050781241\" y=\"28.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"40px\" font-family=\"Arial\">×</text><rect x=\"7307.807812499991\" y=\"5.0\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"7327.807812499991\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"18px\" font-family=\"Arial\">H</text></svg>"
          },
          "metadata": {},
          "execution_count": 153
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x540 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Circuit #9 - design and results"
      ],
      "metadata": {
        "id": "AZHzNo1t6U7C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SVGCircuit(experiment_reveal(experiment_log, 9))"
      ],
      "metadata": {
        "id": "DzGdRmRU6U7H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "outputId": "3cbbe929-5a67-45b3-e721-3423ca55291d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<cirq.contrib.svg.svg.SVGCircuit at 0x7f181e382e90>"
            ],
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y=\"155.0\" width=\"77.9833984375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"444.57974609375003\" y=\"175.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">YY^(YY-2)</text><rect x=\"405.58804687500003\" y=\"5.0\" width=\"77.9833984375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"444.57974609375003\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">YY</text><rect x=\"503.57144531250003\" y=\"205.0\" width=\"77.9833984375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"542.56314453125\" y=\"225.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">YY^(YY-3)</text><rect x=\"503.57144531250003\" y=\"5.0\" width=\"77.9833984375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"542.56314453125\" y=\"25.0\" 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width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"745.5386914062501\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"806.866640625\" y=\"155.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"848.19458984375\" y=\"175.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-2)</text><rect x=\"806.866640625\" y=\"5.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"848.19458984375\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"909.5225390625001\" y=\"205.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"950.8504882812501\" y=\"225.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-3)</text><rect x=\"909.5225390625001\" y=\"5.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"950.8504882812501\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><circle cx=\"1058.74107421875\" cy=\"75.0\" r=\"10.0\" /><rect x=\"1012.1784375000001\" y=\"5.0\" width=\"93.12527343750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1058.74107421875\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">X^(CNOT-0)</text><circle cx=\"1171.86634765625\" cy=\"125.0\" r=\"10.0\" /><rect x=\"1125.3037109375\" y=\"5.0\" width=\"93.12527343750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1171.86634765625\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">X^(CNOT-1)</text><circle cx=\"1284.99162109375\" cy=\"175.0\" r=\"10.0\" /><rect x=\"1238.428984375\" y=\"5.0\" width=\"93.12527343750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1284.99162109375\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">X^(CNOT-2)</text><circle cx=\"1398.11689453125\" cy=\"225.0\" r=\"10.0\" /><rect x=\"1351.5542578125\" y=\"5.0\" width=\"93.12527343750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1398.11689453125\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">X^(CNOT-3)</text><rect x=\"1464.67953125\" y=\"5.0\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1484.67953125\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"18px\" font-family=\"Arial\">H</text></svg>"
          },
          "metadata": {},
          "execution_count": 154
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x540 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Circuit #10 - design and results"
      ],
      "metadata": {
        "id": "8glb7BvI6twm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SVGCircuit(experiment_reveal(experiment_log, 10))"
      ],
      "metadata": {
        "id": "MMj9DQoT6tws",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "89d0a9b5-e63c-4022-90da-eb8ca8cbe2e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<cirq.contrib.svg.svg.SVGCircuit at 0x7f181c6e36d0>"
            ],
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2): </text><rect x=\"10.0\" y=\"805.0\" width=\"59.62125\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"0\" /><text x=\"39.810625\" y=\"825.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">(3, 3): </text><rect x=\"89.62125\" y=\"5.0\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"109.62125\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"18px\" font-family=\"Arial\">X</text><rect x=\"149.62125\" y=\"5.0\" width=\"40\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"169.62125\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"18px\" font-family=\"Arial\">H</text><rect x=\"209.62125\" y=\"55.0\" width=\"77.9833984375\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"248.61294921875\" y=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" 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stroke-width=\"1\" /><text x=\"1339.7911718750001\" y=\"625.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">YY^(YY-11)</text><rect x=\"1296.3458593750001\" y=\"5.0\" width=\"86.890625\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1339.7911718750001\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">YY</text><rect x=\"1403.2364843750001\" y=\"655.0\" width=\"86.890625\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1446.6817968750001\" y=\"675.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">YY^(YY-12)</text><rect x=\"1403.2364843750001\" y=\"5.0\" width=\"86.890625\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1446.6817968750001\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">YY</text><rect x=\"1510.1271093750001\" y=\"705.0\" width=\"86.890625\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1553.5724218750001\" y=\"725.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">YY^(YY-13)</text><rect x=\"1510.1271093750001\" y=\"5.0\" width=\"86.890625\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1553.5724218750001\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">YY</text><rect x=\"1617.0177343750001\" y=\"755.0\" width=\"86.890625\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"1660.4630468750001\" y=\"775.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">YY^(YY-14)</text><rect x=\"1617.0177343750001\" y=\"5.0\" width=\"86.890625\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" 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font-family=\"Arial\">XX</text><rect x=\"2241.4225781250007\" y=\"255.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2282.7505273437505\" y=\"275.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-4)</text><rect x=\"2241.4225781250007\" y=\"5.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2282.7505273437505\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"2344.078476562501\" y=\"305.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2385.4064257812506\" y=\"325.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-5)</text><rect x=\"2344.078476562501\" y=\"5.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2385.4064257812506\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"2446.734375000001\" y=\"355.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2488.0623242187507\" y=\"375.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX^(XX-6)</text><rect x=\"2446.734375000001\" y=\"5.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2488.0623242187507\" y=\"25.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" font-family=\"Arial\">XX</text><rect x=\"2549.390273437501\" y=\"405.0\" width=\"82.65589843750001\" height=\"40\" stroke=\"black\" fill=\"white\" stroke-width=\"1\" /><text x=\"2590.718222656251\" y=\"425.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" font-size=\"14px\" 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          },
          "metadata": {},
          "execution_count": 155
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x540 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Circuit #11 - design and results"
      ],
      "metadata": {
        "id": "GqSk7Dq2pdEF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SVGCircuit(experiment_reveal(experiment_log, 11))"
      ],
      "metadata": {
        "id": "B6pxCMUZ6Ukh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "outputId": "f9386ff6-71b6-44f7-a726-32770e2d10f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-156-59945d57f7b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mSVGCircuit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexperiment_reveal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexperiment_log\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m11\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-72-eabc055b8a98>\u001b[0m in \u001b[0;36mexperiment_reveal\u001b[0;34m(log, run)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0max1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max2\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpyplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m   \u001b[0mpyplot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'MSE Loss and Accuracy of %s gated Parametrized Quantum Circuit'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'gates'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontdict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'fontsize'\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m   \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'history'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'loss'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 11"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Circuit #12 - design and results"
      ],
      "metadata": {
        "id": "nlrANJYxphU6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SVGCircuit(experiment_reveal(experiment_log, 12))"
      ],
      "metadata": {
        "id": "uqB59fR39LZU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Circuit #13 - design and results"
      ],
      "metadata": {
        "id": "ViKdyp0ypjUV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SVGCircuit(experiment_reveal(experiment_log, 13))"
      ],
      "metadata": {
        "id": "LKHgPV_J9LiP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Circuit #14 - design and results"
      ],
      "metadata": {
        "id": "agbzo32vpl8H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SVGCircuit(experiment_reveal(experiment_log, 14))"
      ],
      "metadata": {
        "id": "wM_oF6YO9Lr8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Circuit #15 - design and results"
      ],
      "metadata": {
        "id": "sM3rsi4DpoOd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SVGCircuit(experiment_reveal(experiment_log, 14))"
      ],
      "metadata": {
        "id": "P5Bz--AU9LzH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "5a4qyI4qUUES"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "u_KJtDFfUULF"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}